The nearby elliptical galaxy M87 contains one of the only two supermassive black holes whose emission surrounding the event horizon has been imaged by the Event Horizon Telescope EHT. In 2018 more than two dozen multiwavelength MWL facilities from radio to gammaray energies took part in the second M87 EHT campaign. The goal of this extensive MWL campaign was to better understand the physics of the accreting black hole M87 the relationship between the inflow and inner jets and the highenergy particle acceleration. Understanding the complex astrophysics is also a necessary first step towards performing further tests of general relativity. The MWL campaign took place in April 2018 overlapping with the EHT M87 observations. We present a new contemporaneous spectral energy distribution SED ranging from radio to very high energy VHE gammarays as well as details of the individual observations and light curves. We also conduct phenomenological modelling to investigate the basic source properties. We present the first VHE gammaray flare from M87 detected since 2010. The flux above 350 GeV has more than doubled within a period of about 36 hours. We find that the Xray flux is enhanced by about a factor of two compared to 2017 while the radio and millimetre core fluxes are consistent between 2017 and 2018. We detect evidence for a monotonically increasing jet position angle that corresponds to variations in the bright spot of the EHT image. Our results show the value of continued MWL monitoring together with precision imaging for addressing the origins of highenergy particle acceleration. While we cannot currently pinpoint the precise location where such acceleration takes place the new VHE gammaray flare already presents a challenge to simple onezone leptonic emission model approaches and emphasises the need for combined image and spectral modelling.
['Astrophysics - High Energy Astrophysical Phenomena', 'Astrophysics - Astrophysics of Galaxies']
Broadband Multiwavelength Properties of M87 during the 2018 EHT Campaign including a Very High Energy Flaring Episode
{'Broadband multi-wavelength properties of M87 during the 2018 EHT campaign including a very high energy flaring episode ⋆': "J. C. Algaba 1 , M. Balokovi'c 2 , 3 , S. Chandra 4 , 5 , W.-Y. Cheong 6 , 7 , Y.-Z. Cui ( 崔 玉 竹 ) 8 , F. D'Ammando 9 , A. D. Falcone 10 , N. M. Ford 11 , 12 , M. Giroletti 9 , C. Goddi 13 , 14 , M. A. Gurwell 15 , K. Hada 16 , 17 , 18 , D. Haggard 11 , 19 , S. Jorstad 20 , A. Kaur 21 , T. Kawashima 22 , S. Kerby 21 , J.-Y. Kim 6 , 23 , M. Kino 24 , 25 , E. V. Kravchenko 9 , S.-S. Lee 6 , R.-S. Lu ( 路 如 森 ) 26 , 27 , 23 , S. Marko ff 28 , 29 , J. Michail 30 , 31 , 15 , J. Neilsen 32 , M. A. Nowak 33 , G. Principe ⋆⋆ 34 , 35 , 9 , V. Ramakrishnan 36 , ?? , B. Ripperda 38 , 39 , M. Sasada 40 , S. S. Savchenko , C. Sheridan 32 (The Event Horizon Telescope - Multi-wavelength science working group)  \nK. Akiyama 41 , 24 , 42 , A. Alberdi 43 , W. Alef 44 , R. Anantua 42 , 15 , 45 , K. Asada 46 , R. Azulay 47 , 48 , 44 , U. Bach 44 , A.-K. Baczko 49 , 44 , D. Ball 50 , B. Bandyopadhyay 51 , J. Barrett 41 , M. Bauböck 52 , B. A. Benson 53 , 54 , D. Bintley 55 , 56 , L. Blackburn 42 , 15 , R. Blundell 15 , K. L. Bouman 57 , G. C. Bower 58 , 59 , H. Boyce 60 , 61 , M. Bremer 62 , R. Brissenden 42 , 15 , S. Britzen 44 , A. E. Broderick 63 , 64 , 65 , D. Broguiere 62 , T. Bronzwaer 13 , S. Bustamante 66 , J. E. Carlstrom 67 , 54 , 68 , 69 , A. Chael 70 , C.-k. Chan 50 , 71 , 72 , D. O. Chang 42 , 15 , K. Chatterjee 42 , 15 , S. Chatterjee 73 , M.-T. Chen 58 , Y. Chen ( 陈 永 军 ) 26 , 27 , X. Cheng 6 , I. Cho 43 , 6 , 74 , P. Christian 75 , N. S. Conroy 76 , 15 , J. E. Conway 49 , T. M. Crawford 54 , 67 , G. B. Crew 41 , A. Cruz-Osorio 77 , 78 , R. Dahale 43 , J. Davelaar 81 , 82 , 13 , M. De Laurentis 83 , 84 , R. Deane 85 , 86 , 87 , J. Dempsey 55 , 56 , 88 , G. Desvignes 44 , 89 , J. Dexter 90 , V. Dhruv 52 , I. K. Dihingia 80 , S. S. Doeleman 42 , 15 , S. A. Dzib 62 , 44 , R. P. Eatough 91 , 44 , R. Emami 15 , H. Falcke 13 , J. Farah 92 , 93 , V. L. Fish 41 , E. Fomalont 94 , H. A. Ford 50 , M. Foschi 43 , R. Fraga-Encinas 13 , W. T. Freeman 95 , 96 , P. Friberg 55 , 56 , C. M. Fromm 97 , 78 , 44 , A. Fuentes 43 , P. Galison 42 , 98 , 99 , C. F. Gammie 52 , 76 , 100 , R. García 62 , O. Gentaz 62 , B. Georgiev 50 , R. Gold 102 , A. I. Gómez-Ruiz 103 , 104 , J. L. Gómez 43 , M. Gu ( 顾 敏 峰 ) 26 , 105 , R. Hesper 106 , D. Heumann 50 , L. C. Ho ( 何 子 山 ) 107 , 108 , P. Ho 46 , 56 , 55 , M. Honma 16 , 17 , 109 , C.-W. L. Huang 46 , L. Huang ( 黄 磊 ) 26 , 105 , D. H. Hughes 103 , S. Ikeda 24 , 110 , 111 , 112 , C. M. V. Impellizzeri 113 , 94 , M. Inoue 46 , S. Issaoun 15 , D. J. James 114 , 115 , B. T. Jannuzi 50 , M. Janssen 13 , 44 , B. Jeter 46 , W. Jiang ( 江 悟 ) 26 , A. Jiménez-Rosales 13 , M. D. Johnson 42 , 15 , A. C. Jones 54 , A. V. Joshi 52 , T. Jung 6 , 7 , R. Karuppusamy 44 , G. K. Keating 15 , M. Kettenis 116 , D.-J. Kim 41 , J. Kim 6 , J. Kim 117 , J. Y. Koay 46 , P. Kocherlakota 78 , Y. Kofuji 16 , 109 , P. M. Koch 46 , S. Koyama 118 , 46 , C. Kramer 62 , J. A. Kramer 44 , M. Kramer 44 , T. P. Krichbaum 44 , C.-Y. Kuo 119 , 46 , N. La Bella 13 , A. Levis 57 , Z. Li ( 李 志 远 ) 120 , 121 , R. Lico 122 , 43 , G. Lindahl 15 , M. Lindqvist 49 , M. Lisakov 44 , J. Liu ( 刘 俊 ) 44 , K. Liu 44 , E. Liuzzo 123 , W.-P. Lo 46 , 124 , A. P. Lobanov 44 , L. Loinard 125 , C. J. Lonsdale 41 , A. E. Lowitz 50 , N. R. MacDonald 44 , J. Mao ( 毛 基 荣 ) 126 , 127 , 128 , N. Marchili 123 , 44 , D. P. Marrone 50 , A. P. Marscher 20 , I. Martí-Vidal 47 , 48 , S. Matsushita 46 , L. D. Matthews 41 , L. Medeiros 129 , K. M. Menten 44 , I. Mizuno 55 , 56 , Y. Mizuno 80 , 130 , 78 , J. Montgomery 61 , 54 , J. M. Moran 42 , 15 , K. Moriyama 78 , 16 , M. Moscibrodzka 13 , W. Mulaudzi 28 , C. Müller 44 , 13 , H. Müller 44 , A. Mus 47 , 48 , G. Musoke 28 , 13 , I. Myserlis 131 , H. Nagai 24 , 17 , N. M. Nagar 51 , D. G. Nair 51 , M. Nakamura 132 , 46 , G. Narayanan 66 , I. Natarajan 15 , 42 , A. Nathanail 133 , 78 , S. Navarro Fuentes 131 , C. Ni 64 , 65 , 63 , J. Oh 116 , H. Okino 16 , 109 , H. Olivares 13 , T. Oyama 16 , F. Özel 134 , D. C. M. Palumbo 42 , 15 , G. Filippos Paraschos 44 , J. Park 135 , H. Parsons 55 , 56 , N. Patel 15 , U.-L. Pen 46 , 63 , 136 , 137 , 138 , D. W. Pesce 15 , 42 , V. Piétu 62 , A. PopStefanija 66 , O. Porth 28 , 78 , B. Prather 52 , D. Psaltis 134 , H.-Y. Pu 139 , 140 , 46 , R. Rao 15 , M. G. Rawlings 142 , 55 , 56 , A. W. Raymond 42 , 15 , L. Rezzolla 78 , 143 , 144 , A. Ricarte 42 , 15 , F. Roelofs 15 , 42 , 13 , C. Romero-Cañizales 46 , E. Ros 44 , A. Roshanineshat 50 , H. Rottmann 44 , A. L. Roy 44 , I. Ruiz 131 , C. Ruszczyk 41 , K. L. J. Rygl 123 , S. Sánchez 131 , D. Sánchez-Argüelles 103 , 104 , M. Sánchez-Portal 131 , K. Satapathy 50 , T. Savolainen 146 , 141 , 44 , F. P. Schloerb 66 , J. Schonfeld 15 , K.-F. Schuster 62 , L. Shao 108 , 44 , Z. Shen ( 沈 志强 ) 26 , 27 , D. Small 116 , B. W. Sohn 6 , 7 , 74 , J. SooHoo 41 , L. D. Sosapanta Salas 28 , K. Souccar 66 , J. S. Stanway 147 , H. Sun ( 孙 赫 ) 148 , 149 , F. Tazaki 150 , A. J. Tetarenko 151 , P. Tiede 15 , 42 , R. P. J. Tilanus 50 , 13 , 113 , 152 , M. Titus 41 , K. Toma 153 , 154 , P. Torne 131 , 44 , T. Toscano 43 , E. Traianou 43 , 44 , T. Trent 50 , S. Trippe 155 , M. Turk 76 , I. van Bemmel 116 , H. J. van Langevelde 116 , 113 , 156 , D. R. van Rossum 13 , J. Vos 13 , J. Wagner 44 , D. Ward-Thompson 147 , J. Wardle 157 , J. E. Washington 50 , J. Weintroub 42 , 15 , R. Wharton 44 , M. Wielgus 44 , K. Wiik 158 , G. Witzel 44 , M. F. Wondrak 13 , 159 , G. N. Wong 160 , 70 , Q. Wu ( 吴 庆 文 ) 161 , N. Yadlapalli 57 , P. Yamaguchi 15 , A. Yfantis 13 , D. Yoon 28 , A. Young 13 , Z. Younsi 162 , 78 , W. Yu ( 于 威 ) 15 , F. Yuan ( 袁 峰 ) 163 , Y.-F. Yuan ( 袁 业 飞 ) 164 , J. A. Zensus 44 , S. Zhang 165 , G.-Y. Zhao 43 , 44 , S.-S. Zhao ( 赵 杉杉 ) 26 (The Event Horizon Telescope Collaboration)  \nR. Bellazzini 166 , B. Berenji 167 , E. Bissaldi 168 , 169 , R. D. Blandford 170 , R. Bonino 171 , 172 , P. Bruel 173 , R. A. Cameron 170 , P. A. Caraveo 174 , E. Cavazzuti 175 , C. C. Cheung 176 , S. Ciprini 177 , 178 , P. Cristarella Orestano 179 , 180 , S. Cutini 180 , N. Di Lalla 170 , A. Dinesh 181 , L. Di Venere 169 , A. Domínguez 181 , S. J. Fegan 173 , A. Franckowiak 182 , Y. Fukazawa 183 , P. Fusco 168 , 169 , F. Gargano 169 , C. Gasbarra 177 , 184 , S. Germani 179 , M. Giliberti 169 , 168 , I. A. Grenier 185 , E. Hays 186 , D. Horan 173 , M. Kuss 166 , S. Larsson 187 , 188 , I. Liodakis 189 , F. Longo 34 , 35 , F. Loparco 168 , 169 , M. N. Lovellette 190 , S. Maldera 171 , M. N. Mazziotta 169 , I.Mereu 180 , 179 , P. F. Michelson 170 , N. Mirabal 186 , 191 , T. Mizuno 192 , M. E. Monzani 170 , 193 , A. Morselli 177 , M. Negro 194 , N. Omodei 170 , E. Orlando 35 , 34 , 170 , M. Persic 35 , 195 , S. Rainò 168 , 169 , B. Rani 186 , 191 , A. Reimer 196 , O. Reimer 196 , M. Sánchez-Conde 197 , 198 , P. M. Saz Parkinson 199 , C. Sgrò 166 , E. J. Siskind 200 , P. Spinelli 168 , 169 , D. J. Suson 201 , H. Tajima 202 , 203 , D. F. Torres 204 , 205 , G. Zaharijas 206 (The Fermi Large Area Telescope Collaboration)  \nF. Aharonian 207 , 208 , 209 , F. Ait Benkhali 211 , J. Aschersleben 212 , H. Ashkar 213 , M. Backes 234 , 4 , V. Barbosa Martins ⋆⋆⋆ 214 , 182 , R. Batzofin 215 , Y. Becherini 216 , 217 , D. Berge 214 , 218 , M. Böttcher 4 , C. Boisson 219 , J. Bolmont 220 , M. de Bony de Lavergne 221 , J. Borowska 218 , M. Bouyahiaoui 208 , F. Bradascio 221 , R. Brose 207 , A. Brown 222 , B. Bruno 223 , T. Bulik 224 , C. Burger-Scheidlin 207 , S. Casanova 225 , R. Cecil 226 , J. Celic 223 , M. Cerruti 216 , T. Chand 4 , A. Chen 227 , J. Chibueze 4 , O. Chibueze 4 , G. Cotter 222 , J. Damascene Mbarubucyeye 214 , J. Devin 228 , J. Djuvsland 208 , A. Dmytriiev 4 , S. Einecke 229 , J.-P. Ernenwein 230 , K. Feijen 216 , G. Fontaine 213 , S. Funk 223 , S. Gabici 216 , D. Glawion 223 , J.F. Glicenstein 221 , P. Goswami 216 , G. Grolleron 220 , L. Haerer 208 , B. Heß 231 , T. L. Holch 214 , M. Holler 232 , D. Horns 226 , Zhiqiu Huang 208 , M. Jamrozy 233 , F. Jankowsky 211 , I. Jung-Richardt 223 , E. Kasai 234 , K. Katarzy'nski 235 , R. Khatoon 4 , B. Khélifi 216 , W. Klu'zniak 236 , Nu. Komin 227 , K. Kosack 221 , A. Kundu 4 , R.G. Lang 223 , S. Le Stum 230 , F. Leitl 223 , A. Lemière 216 , M. Lemoine-Goumard 237 , J.-P. Lenain 220 , F. Leuschner 231 , A. Luashvili 219 , J. Mackey 207 , D. Malyshev 231 , G. Martí-Devesa 232 , R. Marx 211 , M. Meyer 238 , A. Mitchell 223 , R. Moderski 236 , M.O. Moghadam 215 , L. Mohrmann 208 , A. Montanari 211 , E. Moulin 221 , M. de Naurois 213 , J. Niemiec 225 , P. O'Brien 239 , S. Ohm 214 , E. de Ona Wilhelmi 214 , M. Ostrowski 233 , S. Panny 232 , M. Panter 208 , U. Pensec 220 , S. Pita 216 , G. Pühlhofer ⋆⋆⋆ 231 , A. Quirrenbach 211 , S. Ravikularaman 216 , 208 , A. Reimer 232 , O. Reimer 232 , B. Reville 208 , I. Reis 221 , H. Ren 208 , F. Rieger 208 , G. Roellingho ff 223 , B. Rudak 236 , E. Ruiz-Velasco 208 , K. Sabri 228 , V. Sahakian 210 , H. Salzmann 231 , A. Santangelo 231 , M. Sasaki 223 , J. Schäfer 223 , F. Schüssler 221 , H.M. Schutte 4 , J.N.S. Shapopi 234 , A. Sharma 216 , H. Sol 219 , S. Spencer 223 , Ł. Stawarz 233 , C. Steppa 215 , K. Streil 223 , H. Suzuki 240 , T. Takahashi 241 , T. Tanaka 240 , A.M. Taylor 214 , R. Terrier 216 , M. Tluczykont 226 , M. Tsirou 214 , C. van Eldik 223 , M. Vecchi 212 , T. Wach 223 , S.J. Wagner 211 , A. Wierzcholska 225 , M. Zacharias 211 , 4 , A.A. Zdziarski 236 , A. Zech 219 , N. ˙ Zywucka 4  \n(H.E.S.S. Collaboration)  \nS. Abe 242 , J. Abhir 243 , A. Abhishek 244 , V. A. Acciari 245 , A. Aguasca-Cabot 246 , I. Agudo 247 , T. Aniello 248 , S. Ansoldi 249 , 282 , L. A. Antonelli 248 , A. Arbet Engels 250 , C. Arcaro 251 , M. Artero 245 , K. Asano 242 , A. Babi'c 252 , U. Barres de Almeida 253 , J. A. Barrio 254 , I. Batkovi'c 251 , A. Bautista 250 , J. Baxter 242 , J. Becerra González 255 , W. Bednarek 256 , E. Bernardini 251 , J. Bernete 257 , A. Berti 250 , J. Besenrieder 250 , C. Bigongiari 248 , A. Biland 243 , O. Blanch 245 , G. Bonnoli 248 , Ž. Bošnjak 252 , E. Bronzini 248 , I. Burelli 249 , G. Busetto 251 , A. Campoy-Ordaz 258 , A. Carosi 248 , R. Carosi 259 , M. Carretero-Castrillo 246 , A. J. Castro-Tirado 247 , D. Cerasole 260 , G. Ceribella 250 , Y. Chai 242 , A. Cifuentes 257 , E. Colombo 245 , J. L. Contreras 254 , J. Cortina 257 , S. Covino 248 , G. D'Amico 261 , V. D'Elia 248 , P. Da Vela 248 , F. Dazzi 248 , A. De Angelis 251 , B. De Lotto 249 , R. de Menezes 262 , M. Delfino 245 , 283 , J. Delgado 245 , 283 , C. Delgado Mendez 257 , F. Di Pierro 262 , R. Di Tria 260 , L. Di Venere 260 , D. Dominis Prester 263 , A. Donini 248 , D. Dorner 264 , M. Doro 251 , D. Elsaesser 265 , J. Escudero 247 , L. Fariña 245 , A. Fattorini 265 , L. Fo ff ano 248 , L. Font 258 , S. Fröse 265 , S. Fukami 243 , Y. Fukazawa 266 , R. J. García López 255 , M. Garczarczyk 214 , S. Gasparyan 267 , M. Gaug 258 , J. G. Giesbrecht Paiva 253 , N. Giglietto 260 , F. Giordano 260 , P. Gliwny 256 , N. Godinovi'c 268 , T. Gradetzke 265 , R. Grau 245 , D. Green 250 , J. G. Green 250 , P. Günther 264 , D. Hadasch 242 , A. Hahn ⋆⋆⋆⋆ 250 , T. Hassan 257 , L. Heckmann 250 , J. Herrera Llorente 255 , D. Hrupec 269 , R. Imazawa 266 , K. Ishio 256 , I. Jiménez Martínez 250 , J. Jormanainen 270 , T. Kayanoki 266 , D. Kerszberg 245 , G. W. Kluge 261 , 284 , Y. Kobayashi 242 , P. M. Kouch 270 , H. Kubo 242 , J. Kushida 271 , M. Láinez 254 , A. Lamastra 248 , F. Leone 248 , E. Lindfors 270 , S. Lombardi 248 , R. López-Coto 247 , M. López-Moya 254 , A. López-Oramas 255 , S. Loporchio 260 , A. Lorini 244 , E. Lyard 272 , B. Machado de Oliveira Fraga 253 , P. Majumdar 273 , M. Makariev 274 , G. Maneva 274 , M. Manganaro 263 , S. Mangano 257 , K. Mannheim 264 , M. Mariotti 251 , M. Martínez 245 , M. Martínez-Chicharro 257 , A. Mas-Aguilar 254 , D. Mazin ⋆⋆⋆⋆ 242 , 285 , S. Menchiari 244 , S. Mender 265 , D. Miceli 251 , T. Miener 254 , J. M. Miranda 244 , R. Mirzoyan 250 , M. Molero González 255 , E. Molina 255 , H. A. Mondal 273 , A. Moralejo 245 , D. Morcuende 247 , T. Nakamori 275 , C. Nanci 248 , V. Neustroev 276 , L. Nickel 265 , M. Nievas Rosillo 255 , C. Nigro 245 , L. Nikoli'c 244 , K. Nilsson 270 , K. Nishijima 271 , T. Njoh Ekoume 245 , K. Noda 277 , S. Nozaki 250 , Y. Ohtani 242 , A. Okumura 278 , J. Otero-Santos 247 , S. Paiano 248 , D. Paneque 250 , R. Paoletti 244 , J. M. Paredes 246 , M. Peresano 250 , M. Persic 249 , 286 , M. Pihet 251 , G. Pirola 250 , F. Podobnik 244 , P. G. Prada Moroni 259 , E. Prandini 251 , C. Priyadarshi 245 , M. Ribó 246 , J. Rico 245 , C. Righi 248 , N. Sahakyan 267 , T. Saito 242 , F. G. Saturni 248 , K. Schmidt 265 , F. Schmuckermaier 250 , J. L. Schubert 265 , T. Schweizer 250 , A. Sciaccaluga 248 , G. Silvestri 251 , J. Sitarek 256 , V. Sliusar 272 , D. Sobczynska 256 , A. Spolon 251 , A. Stamerra 248 , J. Striškovi'c 269 , D. Strom 250 , M. Strzys 242 , Y. Suda 266 , S. Suutarinen 270 , H. Tajima 278 , M. Takahashi 278 , R. Takeishi 242 , F. Tavecchio 248 , P. Temnikov 274 , K. Terauchi 279 , T. Terzi'c 263 , M. Teshima 250 , 287 , S. Truzzi 244 , A. Tutone 248 , S. Ubach 258 , J. van Scherpenberg 250 , M. Vazquez Acosta 255 , S. Ventura 244 , G. Verna 244 , I. Viale 251 , C. F. Vigorito 262 , V. Vitale 280 , I. Vovk 242 , R. Walter 272 , M. Will 250 , C. Wunderlich 244 , T. Yamamoto 281  \n(MAGIC Collaboration)  \nA. Acharyya 288 , C. B. Adams 289 , P. Bangale 290 , J. T. Bartkoske 291 , W. Benbow 15 , J. L. Christiansen 292 , A. Duerr 291 , M. Errando 293 , Q. Feng 291 , G. M. Foote 290 , L. Fortson 295 , A. Furniss 294 , W. Hanlon 15 , O. Hervet 296 , C. E. Hinrichs 15 , 301 , J. Holder 290 , T. B. Humensky 302 , 316 , W. Jin † 306 , 315 , M. N. Johnson 296 , P. Kaaret 297 , M. Kertzman 299 , D. Kieda 291 , T. K. Kleiner 214 , N. Korzoun 290 , F. Krennrich 298 , S. Kumar 302 , M. J. Lang 303 , M. Lundy 304 , G. Maier 214 , C. E McGrath 300 , M. J. Millard 297 , C. L. Mooney 290 , P. Moriarty 303 , R. Mukherjee 314 , W. Ning 306 , S. O'Brien 304 , 307 , R. A. Ong 306 , M. Pohl 308 , 214 , E. Pueschel 309 , J. Quinn 310 , K. Ragan 304 , P. T. Reynolds 311 , D. Ribeiro 295 , E. Roache 15 , J. L. Ryan 306 , I. Sadeh 214 , L. Saha 15 , M. Santander ¶315 , G. H. Sembroski 305 , R. Shang 314 , M. Splettstoesser 296 , A. K. Talluri 295 , J. V. Tucci 312 , J. Valverde 313 , 316 , V. V. Vassiliev 306 , D. A. Williams 296 , S. L. Wong 304  \n(VERITAS Collaboration) Z. Chen 26 , 317 , L. Cui 318 , 317 , T. Hirota 16 , 17 , B. Li 26 , 317 , G. Li 318 , Q. Liu 26 , 317 , X. Liu 318 , 317 , Z. Liu 318 , J. Ma 318 , K. Niinuma 319 , 320 , H. Ro 6 , N. Sakai 322 , S. Sawada-Satoh 323 , K. Wajima 321 , re f inst 5 , J. Wang 26 , 317 , N. Wang 318 , 317 , B. Xia 26 , 317 , H. Yan 318 , Y. Yonekura 324 , H. Zhang 318 , R. Zhao 26 , 317 , and W. Zhong 26 , 317  \n(EAVN Collaboration)  \n(A ffi liations can be found after the references)  \nReceived April 26, 2024; accepted August 29, 2024", 'ABSTRACT': 'Context. The nearby elliptical galaxy M87 contains one of only two supermassive black holes whose emission surrounding the event horizon has been imaged by the Event Horizon Telescope (EHT). In 2018, more than two dozen multi-wavelength (MWL) facilities (from radio to γ -ray energies) took part in the second M87 EHT campaign.  \nAims. The goal of this extensive MWL campaign was to better understand the physics of the accreting black hole M87*, the relationship between the inflow and inner jets, and the high-energy particle acceleration. Understanding the complex astrophysics is also a necessary first step towards performing further tests of general relativity.  \nMethods. The MWL campaign took place in April 2018, overlapping with the EHT M87* observations. We present a new, contemporaneous spectral energy distribution (SED) ranging from radio to very high-energy (VHE) γ -rays as well as details of the individual observations and light curves. We also conducted phenomenological modelling to investigate the basic source properties.  \nResults. We present the first VHE γ -ray flare from M87 detected since 2010. The flux above 350 GeV more than doubled within a period of ≈ 36 hours. We find that the X-ray flux is enhanced by about a factor of two compared to 2017, while the radio and millimetre core fluxes are consistent between 2017 and 2018. We detect evidence for a monotonically increasing jet position angle that corresponds to variations in the bright spot of the EHT image.  \nConclusions. Our results show the value of continued MWL monitoring together with precision imaging for addressing the origins of high-energy particle acceleration. While we cannot currently pinpoint the precise location where such acceleration takes place, the new VHE γ -ray flare already presents a challenge to simple one-zone leptonic emission model approaches, and it emphasises the need for combined image and spectral modelling.  \nKey words. Accretion, accretion disks - black hole physics -galaxies: individual: M87  \nA & A proofs: manuscript no. output', '1. Introduction': "The M87 elliptical galaxy in the Virgo Cluster is one of the nearest active galactic nuclei (AGN), located at a distance of 16 . 8 ± 0 . 8 Mpc (Blakeslee et al. 2009; Bird et al. 2010; Cantiello et al. 2018, and Event Horizon Telescope Collaboration et al. 2019d). It also harbours one of the largest known supermassive black holes (SMBHs; ∼ 6 . 5 × 10 9 M ⊙ determined by Gebhardt et al. (2011), but see also, e.g., Walsh et al. (2013); Liepold et al. (2023); Osorno et al. (2023); Simon et al. (2024) finding a slightly smaller mass). It is one of only two SMBHs where a ring of light emitted from near the event horizon has been directly imaged at 230 GHz, via the global Very Long Baseline Interferometry (VLBI) project, the Event Horizon Telescope (EHT). The ring's size and shape are directly predicted by general relativity (GR) and depend on the black hole (BH) mass and spin and the orientation of the spin axis with respect to our line of sight (see, e.g., Bardeen 1973; Johannsen & Psaltis 2010).  \nIn April 2019, the EHT Collaboration (EHTC) presented the first M87* images (we note that we use M87* to refer to the SMBH rather than its host galaxy) from the 2017 campaign along with modelling and interpretation of the broadband emission (Event Horizon Telescope Collaboration et al. 2019a,b,c,d,e,f). A primary result of this sequence of works by the EHTC was an independent determination of M87*'s mass: (6 . 5 ± 0 . 7) × 10 9 M ⊙ , consistent with the previous measurements of Gebhardt et al. (2011).  \nFor its modelling, EHTC uses state-of-the-art techniques (see, e.g., Event Horizon Telescope Collaboration et al. 2019e, Porth et al. 2019, Gold et al. 2020, M87 2018 II) that combine ideal GR magnetohydrodynamics (GRMHD) with GR Ray Tracing (GRRT). GRRT is typically conducted as a post-processing step and is highly dependent on the assumed electron distributions that are not determined from first-principles methods. For example, the electrons emitting synchrotron radiation are accelerated by and change the local electromagnetic fields, and they may also interact with the radiation field. The addition of constraints that can guide microphysical kinetic models of particle acceleration is a clear way to reduce the current degeneracy in interpretation.  \nAt the same time, the presence of strong, ordered magnetic fields near the SMBH is required to explain the detected linearly polarised radio synchrotron radiation as well as the angular momentum transport in the inflow and the launching of powerful jets of plasma (see, e.g., Blandford et al. 2019; Event Horizon Telescope Collaboration et al. 2021c). M87* currently expels vast bipolar jets. However, in most observations, only the one that is relativistically beamed towards Earth at approximately 20 · from the line of sight is visible (Mertens et al. 2016), as the jets are accelerated to apparent velocities of up to 6 c (where c is the speed of light) on sub-arcsecond scales (Snios et al. 2019). Previous studies have found that the width and the outflow velocity of the approaching jet vary as a function of the distance from  \nthe SMBH (Asada et al. 2014), and other physical parameters possibly vary in a similar manner (Asada & Nakamura 2012). These jets radiate across the entire electromagnetic spectrum, with contributions to di ff erent parts of the spectral energy distribution (SED) arising at di ff erent places along their elongated structure. A complete model ultimately needs to include both the inflow and outflow components and be able to not only match the EHT image properties but also reproduce the inner jet dynamical properties as well as the overall multi-wavelength (MWL) spectrum.  \nFor the 2017 EHT papers on M87*, complementary MWL information, such as the estimated minimum jet power ( P jet ≥ 10 42 erg s -1 ; e.g. Reynolds et al. 1996; Stawarz et al. 2006; de Gasperin et al. 2012; Prieto et al. 2016), allowed roughly half of the ∼ 45 initial GRMHD models to be ruled out, including all models with zero spin. However, MWL constraints on the SED such as the X-ray luminosity were only applied as an upper limit and thus did not exclude many additional models. In stark contrast, the inclusion of MWL data to compare against SEDs for the second 2017 EHTC image, that was of Sagittarius A* (Sgr A*; the SMBH in the centre of the Milky Way), provided a significantly augmented constraining power (Event Horizon Telescope Collaboration et al. 2022a,b,c,d,e,f; Wielgus et al. 2022). Specifically, the infrared and X-ray observations on Sgr A*'s emission from the Keck Observatory, the Very Large Telescope (VLT), the Chandra X-ray Observatory, and Nuclear Spectroscopic Telescope Array ( NuSTAR ) provided particularly tight constraints and allowed us to downselect from a large number of original synthetic images that were generated from models covering a much wider parameter space range than the original set for M87*.  \nWe have acquired a full set of complementary, contemporaneous MWL observations for M87* to support modelling and interpretation as well as to provide a legacy dataset for the community. Together with the EHT images, these datasets provide rich input to help distinguish between the currently large range of theoretical scenarios for accretion and jet launching as well as for particle acceleration, that powers the emitted radiation. The first paper in this series was published as EHT MWL Science Working Group et al. (2021) (hereafter M87 MWL2017), and it presents the extensive MWL observations carried out alongside the EHT 2017 campaign on M87 by the EHT-MWL Science Working Group. The working group included EHTC members as well members of more than a dozen other partner facilities on the ground and in space. M87 MWL2017 includes VLBI images and spectral index maps at several frequencies lower than the 230 GHz band probed by EHT together with an SED spanning centimetre-band radio through TeV γ -rays, a range of more than 17 decades in frequency. In 2017, this source was found to be in a historically low state at all frequencies, with the core flux dominating over the HST-1 knot at high energies, making it possible to combine the high energy core flux constraints with the more spatially precise VLBI data.  \nIn M87 MWL2017 and the present work, we merge the VLBI and spectral constraints by 'tagging' the radio flux points with VLBI constraints on the emitting region size, a constraint that is often missed by single-zone SED modelling approaches. In both papers we also explore M87*'s jet properties in an order-ofmagnitude approach via two heuristic single-zone models meant to help reveal general trends and allow rough comparisons between basic properties year to year. Single-zone models are a common first approach that often successfully account for most of the optical and higher energy emission in many AGN, particularly blazars. The success of these techniques implies that a  \nsignificant fraction of energy in the jets is converted into particle energy in a relatively localised region. How this process occurs is one of the most urgent and outstanding questions in the field today, relating to identification of the source of very high energy (VHE) particles detected on Earth, such as cosmic rays (CRs) and neutrinos, as well as understanding the electromagnetic counterparts of explosive transient events, such as compact object mergers involving a neutron star. Localisation is thus a key step in constraining the particle acceleration mechanism as well as in investigating the hadronic content of the jet.  \nM87MWL2017, however, demonstrated that M87* requires a stratified jet model, meaning that a single-zone leptonic model cannot adequately describe the sub-millimetre and high-energy SED simultaneously. In particular, we showed that the γ -rays cannot be fit by the same single-zone model that can match the EHT flux and size constraints. While this would seem to exclude scenarios where the γ -rays detected in 2017 in the quiescent (i.e. non-flaring) state are emitted from the same region as the submillimetre radiation in the EHT image, we discuss new advances in the modelling that may indicate otherwise.  \nMeanwhile, a new constraint that has yet to be incorporated into the EHT modelling is the source emission variability. In the EHTC Sgr A* papers, it was shown that the root-mean-square variability is one of the most di ffi cult constraints to satisfy, and in fact, almost all of our GRMHD models are somewhat too variable compared to the data (see Event Horizon Telescope Collaboration et al. 2022g). Based only on the 2017 observations, it was not possible to draw conclusions regarding the variability of M87*. For prograde orbits, the dynamical timescale for light to circle M87*'s innermost stable circular orbit ranges from just under a week to approximately a month, depending on the spin, that is currently unconstrained. Therefore, daily images taken over a single week can be considered to correspond e ff ectively to a single snapshot. However, one year represents a significant jump in time for M87*, roughly ten to 70 dynamical timescales, providing a chance to temporally resolve its uncorrelated states.  \nThe EHTC has recently published the first results from its April 2018 campaign on M87* (M87 2018 I). The 2018 EHT images of M87* reveal an asymmetric ring structure that is brighter in the southeast and with a diameter of ∼ 43 µ as. This is remarkably consistent with the EHT images obtained from the 2017 observations, strongly confirming the presence of a supermassive black hole with a mass M BH ∼ 6 . 5 × 10 9 M ⊙ . The 2018 images show a significant shift in the position angle (by ∼ 30 · ) of the ring brightness asymmetry with respect to that of the 2017 images, suggesting the presence of variations on yearly timescales in the event-horizon-scale structures. However, the compact flux density in the 2018 EHT data is less well constrained than in 2017 due to the more limited coverage of short-to-intermediate baselines. Bayesian image reconstruction methods yield a relatively large range of ∼ 0.5-1.0 Jy within a scale of ≲ 100 µ (see M872018I for a full description of the 2018 EHT observations).  \nDuring the accompanying 2018 MWL campaign, all currently operating ground-based imaging atmospheric Cherenkov telescopes (IACTs; H.E.S.S., MAGIC, and VERITAS, see Sect. 2.4.2) as well as Fermi -LAT measured the first γ -ray flare seen in M87* in eight years. In this paper, we present the results of the 2018 MWL campaign as well as a comparison with the 2017 results. In Sect. 2, we describe the new MWL observations with band-specific results and, where relevant, light curves and comparisons to prior observations, while we report in Appendix B further details on the data processing. In Sect. 3, we present the compiled nearly simultaneous SED of M87* for the 2018 MWL campaign. In Sect. 4, we present the best fits using  \nphenomenological single-zone models similar to those used in M87MWL2017 and discuss indications for structural changes between 2017 and 2018. In Sect. 5, we give our conclusions. All data files and products are available for download, as described in Sect. 3 and in Appendix. Data availability.", '2. Observations and data reduction': 'The 2018 EHT observations of M87 were made in April 2018 using a total of eight stations (in six geographical locations) across the globe. While observations were performed over four nights (21, 22, 25, and 28 April), the second and last sessions su ff ered from various issues (bad weather conditions across the array, poor baseline coverage, etc.). Therefore, reliable EHT images of M87* could only be obtained from the data taken on 21 and 25 April.  \nIn the following subsections we present brief descriptions of the 2018 MWL campaign on M87*- more detailed descriptions including data processing procedures and band-specific analyses appear in Appendix B. To aid readability, tabulated data are collected in Appendix C. Figure 1 shows a schematic overview of the observations carried between March and May 2018 for the second EHT-MWL campaign on M87. In addition, to observations carried during March-May 2018 we considered for our study also global VLBI (carried on 1 February 2018) and HST (carried on 30 July 2018) observations.', '2.1. Radio observations': "Here we outline the radio-millimeter observations obtained during the 2018 campaign with various VLBI facilities and connected interferometers. Following M87 MWL2017, we use the term 'radio core' to represent the innermost part of the radio jet imaged by VLBI. A radio core in a VLBI jet image is conventionally defined as the most compact (often unresolved or partially resolved) feature seen at the apparent base of the radio jet (e.g. Lobanov 1998; Marscher 2008). For this reason, di ff erent angular resolutions by di ff erent VLBI instruments and frequencies, together with the frequency-dependent synchrotron optical depth, can lead to di ff erences in the identification of a radio core in each observation (see also M87 MWL2017, for a related discussion).", '2.1.1. VERA 22 GHz': 'The nucleus of M87 was densely monitored over 2017 and 2018 at 22 GHz with the VLBI Exploration of Radio Astrometry project (VERA; Kobayashi et al. 2003), as part of a regular monitoring program of a sample of γ -ray bright AGN (Nagai et al. 2013). A total of 17 and 13 epochs were obtained in 2017 and 2018, respectively (see Table C.1). During each session, M87 was observed for 10-30 minutes with an allocated bandwidth of 16 MHz, su ffi cient to detect the bright core and create its light curves (additional details appear in Appendix B.1.1). Peak flux density light curves obtained with VERA at 22 / 43 GHz during 2017-2018 are included in Fig. 2.', '2.1.2. EAVN/KaVA 22 and 43 GHz': 'Between January 2017 and June 2018, M87 was regularly monitored at 22 and 43 GHz with the East Asian VLBI Network (EAVN; Cui et al. 2021; Akiyama et al. 2022), a joint VLBI array of radio telescopes in East Asia. A total of 22 (9  \n58200  \nFig. 1. Instrument coverage summary of the 2018 M87 MWL campaign covering MJD range 58198-58270. The grey band indicates the time interval of the VHE γ -ray flare episode.  \n<!-- image -->  \nat 22 GHz and 13 at 43 GHz) and 20 (9 at 22 GHz and 11 at 43 GHz) epochs were obtained in 2017 and 2018, respectively (details in Appendix B.1.2 and Table C.1). Peak flux density light curves obtained with EAVN at 22 and 43 GHz during 2017-2018 are included in Fig. 2.', '2.1.3. VLBA 24 and 43 GHz': 'The program targeting M87 using the National Radio Astronomy Observatory (NRAO) Very Long Baseline Array (VLBA) at central frequencies of 24 and 43 GHz was initiated in 2006 (Walker et al. 2018) and lasted until 2020. Presented here are 10 epochs of observations that were obtained in 2018 and include two imaging (i.e. full-track) and eight short (snapshot) sessions on M87 (see summary in Appendix B.1.3 and Table C.1). Peak flux density light curves obtained with VLBA at 22 and 43 GHz during 2017-2018 are included in Fig. 2.', '2.1.4. GMVA+ALMA 86GHz': 'M87 was observed by the Global Millimetre VLBI Array (GMVA) for the first time in concert with the phased Atacama Large Millimetre / submillimetre Array (ALMA) and the Greenland Telescope (GLT) on 14-15 April 2018 (See details in Appendix B.1.4 and Table C.1).', '2.1.5. KVN 22, 43, 86, and 129 GHz': 'M87 was regularly monitored by the Korean VLBI Network (KVN) at 22, 43, 86 and 129 GHz, as part of the Interferometric Monitoring of Gamma-ray Bright Active galactic nuclei (iMOGABA; Lee et al. 2016) program. More information can be  \nFig. 2. Radio light curves of the M87 core in 2017 and 2018 at multiple bands. The upper and lower panels are for connected interferometers and VLBI, respectively. The corresponding beam sizes are indicated in Table C.1. KVN data at 22 and 43 GHz are not shown here since KVN captures the data from the shortest baselines of EAVN.  \n<!-- image -->  \nMJD  \nfound in Appendix B.1.5 and Table C.1. Peak flux density light curves obtained with KVN at 86 / 129 GHz during 2017-2018 are included in Fig. 2.', '2.1.6. Global 43 GHz VLBI': 'M87 was observed on 1 February 2018 at 43 GHz by a global array of sensitive VLBI antennas in full-track to maximise the imaging sensitivity and resolution. While details of the observation and imaging results will be reported elsewhere (Kim et al., in prep.), we provide a peak flux density of the core and a VLBI-scale total flux density (see Tables C.1 and Table C.1). We estimate these measurements to be uncertain up to ∼ 20% considering the absolute flux calibration error and systematic uncertainties in defining the core and the field-of-view (see also Appendix B.1.6).', '2.1.7. ALMA 93 GHz and 221 GHz': 'Observations with phased-ALMA (Matthews et al. 2018; Goddi et al. 2019; Crew et al. 2023) were conducted as part of the 2018 VLBI campaigns with the GMVA at 3 mm / 93 GHz (ALMA Band 3) and the EHT at 1.3 mm / 221 GHz (ALMA Band 6). The main observational and imaging parameters for the 2018 observations are summarised in Table C.2, while those for 2017 are summarised in M87 MWL2017, as well as included in Fig. 2. For each dataset and corresponding image, the table also reports flux-density values both in the central compact core and the extended jet (see Appendix B.1.7 for more detailed descriptions of data acquisition and reduction).', '2.1.8. SMA 200-270 and 345GHz': "The long term 1.1-1.4 mm band (200-270 GHz) and 0.87 mm band (345 GHz) flux density light curve data for M87 shown in Fig.2, and later in Fig.14 and 16, were obtained at the Submillimeter Array (SMA) near the summit of Mauna Kea (Hawai'i). M87 is included in an ongoing monitoring program at the SMA to determine flux densities for compact extragalactic radio sources that can be used as calibrators at millimetre wavelengths (Gurwell et al. 2007). A summary of the measurements made from these data for 2017 and 2018 is shown in Table C.2. The reported core flux for M87 is the vector average of baselines longer than 30 k λ (see Appendix B.1.8 for more details).", '2.2.1. Optical observations by the Kanata telescope': "Photometric images of M87 were obtained using the 1.5-m Kanata telescope and the Hiroshima Optical and Near-InfraRed Camera (HONIR), that can perform simultaneous two-band imaging (Akitaya et al. 2014). We used optical R C (634.9 nm) and near-infrared J -band (1250 nm) filters for the observations. Images with a field of view of 10 '' × 10 '' and a spatial resolution of 0.294 '' pixel -1 through the band-pass filters were obtained. The central flux of M87 within a radius of 5 '' was measured. The measured flux should include three components: radiation from the central region near the BH, radiation from the HST-1 knot (located at a projected distance of ∼ 70 pc from the core), and thermal radiation from the host galaxy. Thus, the measured flux is an upper limit of the flux of the central nuclear region. Additional details appear in Appendix B.2.1.", '2.2.2. UV and optical observations from Swift -UVOT': "During the Swift pointings, the UVOT instrument observed M87 in its optical ( v , b and u ) and UV ( w 1, m 2 and w 2) photometric  \nbands (Poole et al. 2008; Breeveld et al. 2010). An aperture of 5 '' radius was used for the flux extraction for all UVOT bands. Compared to the values obtained during the EHT 2017 campaign, we observed with UVOT in 2018 an increase of the flux by a factor of ∼ 3 in optical and a factor of ∼ 2 in UV bands. However, the high contribution of the host galaxy flux in the V band and partly in the b band contributes to the large uncertainties on the flux. A detailed description of data, analysis and modelling is given in Appendix B.2.2 (for all the results of the Swift -UVOT observations during the EHT 2018 campaign see Table C.5.).", '2.2.3. UV observation with HST': "We have investigated the HST archive to search for HST UV / optical observations during the 2018 EHT MW campaign. Unfortunately, the HST observation nearest to the campaign was performed more than 3 months later, on 2018 July 30 (MJD 5832.90764). The observations were carried out with the STIS camera (NUV-MAMA detector with F25QTZ aperture) over two HST orbits at the pivot wavelength 2359.7Å (proposal ID 15358). Since none of the other Optical / UV / NUV instruments used in this campaign allow measurements of the core and HST-1 knot fluxes separately at these wavelengths, we have utilised the HST data to obtain fluxes of the core and HST-1 knot.  \nAs in our previous work M87 MWL2017, we have performed photometric decomposition to exclude the host galaxy flux from the image. The host was approximated with two Sersic functions (Sersic 1968) with the core and jet regions masked out. Unfortunately, due to the small FoV of STIS, no stars were available in the images for constructing a PSF. The HST PSF library does not contain PSFs for the STIS instrument, and the HST team advises against using the TinyTim package 1 to build PSFs for STIS images. Nevertheless, we have performed a decomposition without taking into account the PSF, rather by masking the central part of the host galaxy and performing the decomposition of outer parts of the galaxy and extrapolating the result to the centre.  \nFigure 3 shows the residual image (after host model subtraction), where the core and the HST-1 knot are clearly visible. To obtain the fluxes of these features, we performed aperture photometry with an aperture radius of 0 . '' 4 (the same as in M87 MWL2017). Table C.6 gives the fluxes of the core and HST-1 knot before and after host model subtraction. The uncertainties of fluxes before correction for the host galaxy were calculated based on the STIS documentation 2 , while we assume uncertainties of the corrected fluxes of 50%, taking into account the method of the decomposition. However, as can be seen in Table C.6, contributions of the host galaxy are small since the band is blue, while the host galaxy is an old elliptical type. The results indicate that the HST-1 knot is significantly fainter than the core, that most likely was also the case during the EHT MW campaign in 2018.", '2.2.4. Far-UV observations from AstroSat-UVIT': "The UV Imaging Telescope (UVIT) instrument on board the AstroSat mission consists of twin telescopes, each of 38 cm diam-  \n1  \nwww.stsci.edu/files/live/sites/www/files/home/hst/  \ninstrumentation/focus-and-pointing/documentation/  \n\\_documents/tinytim.pdf  \n2  \nhttps://hst-docs.stsci.edu/stisdhb/  \nchapter-4-stis-error-sources/  \n4-2-summary-of-accuracies  \nFig. 4. AstroSat-UVIT photometric cut of the decomposition model along the jet direction. The thick black line shows the observed flux along the cut, the blue line is the central point source, the orange line is the host galaxy, the green line is the jet total flux, and the red line is the total model flux. The shaded area shows the masked out region of the jet that has a complex fine structure that cannot be accurately modelled with a simple analytical function and was therefore excluded from the decomposition to reduce the model complexity.  \n<!-- image -->  \nFig. 3. Hubble Space Telescope image of M87 at 2359.7Å with the host galaxy subtracted. The core and HST-1 are indicated; the distance between the features is 1.'00 ± 0.'05, and the pixel size is 0.'0248.  \n<!-- image -->  \neter, one dedicated to far-UV (FUV: λ = 1300 - 1800 Å) and the other to near-UV (NUV: λ = 2000 - 3000 Å) & Visible (VIS: λ = 3200 - 5500 Å) channels (Kumar et al. 2012). In this work, the far-UV images from UVIT are used to constrain the jet and the HST-1 knot component in the UV emission. The images created for each orbit of the satellite were aligned and merged to create the final image with an e ff ective exposure time of 14.1 ks. The flux contributions of di ff erent known components of M87 are extracted by 2D image decomposition modelling. Figure 4 shows the photometric slice made through the galaxy centre in the jet direction obtained in the BaF2 band (F154W: λ e f f ∼ 1541 Å, with ∆ λ e f f ∼ 380 Å). In this figure the observed galaxy flux is shown with a thick black line, the total model (a sum of all components) with a red line, the host galaxy model with an orange line, the total jet model, that include the asymmetric Core-Sersic model (Graham & Driver 2005) and a point source for the HST1 knot, is shown with a green line, and the central source with a blue line. A detailed description of data, analysis and modelling is given in Appendix B.2.3.", '2.3.1. Chandra and NuSTAR observations and joint spectral analysis': "Observations of M87 with the Advanced CCD Imaging Spectrometer (ACIS) on board the Chandra X-ray Observatory were collected via Director's Discretionary Time (DDT; PI: Wong) on 22 April 2018 (ObsID 21075 for 9.1 ks starting at UT 00:09) and 24 April 2018 (ObsID 21076 for 9.0 ks starting at UT 13:20). An observation with NuSTAR (Harrison et al. 2013) was also obtained via DDT on 25 April 2018 (ObsID 60466002002 for 21.1 ks beginning at UT 13:56), su ffi ciently close in time to the second Chandra observation that they can be considered contemporaneous. The data from these observations have been included in previous studies of X-ray flux variability in M87 described in Yang et al. (2019) and Imazawa et al. (2021).  \nFollowing the procedure in M87 MWL2017, the two Chandra observations are combined in our spectral analysis such that the reported fluxes represent the average for 22-25 April 2018. The variability of the background-subtracted count rates for each  \nof the spatial components is shown in Fig. 5. As is clear from the Chandra light curve, the core was significantly brighter in both observations than in April 2017. No significant variability is detected within either of the 2018 observations. However, the core flux did increase between the two observations separated by approximately two days, while the other components remained steady (see Fig. 5). Moreover, an increase in the X-ray brightness of M87 relative to 2017 is apparent directly from the NuSTAR spectrum, that exhibits higher S / N out to ≃ 60 keV than the 2017 data (c.f. M87 MWL2017, Fig. 9), despite equal exposure time.  \nFig. 5. X-ray light curves of the spatially resolved components of M87 from the Chandra observations in 2018. The core (orange) shows clear variability between the two di ff erent days on which observations have been carried out, while the HST-1 knot and the rest of the jet remain fairly steady.  \n<!-- image -->  \nIn particular, we detect a significant increase in the flux of the core component relative to 2017 observations. The unabsorbed core flux in the 2-10 keV band averaged between the two Chandra observations in April 2018 was 2 . 6 × 10 -12 erg s -1 cm -2 that represents an ∼ 80% increase on a timescale of one year. Assuming a distance of 16.8 Mpc, we find the average 2-10 keV band luminosity of the core to be (8 . 8 ± 0 . 4) × 10 40 erg s -1 , again a marked increase over (4 . 8 ± 0 . 2) × 10 40 erg s -1 observed in 2017. The significantly higher luminosity is not seen in the HST-1 knot or the rest of the jet, that both show ≲ 10% variability. These results are similar to those obtained by Imazawa et al. (2021). Despite the flux increase, we note that there is no evidence for a change in the shape of the spectrum. Our best fit power-law (PL) photon index for the core is Γ core = 2 . 03 + 0 . 12 -0 . 07 , that is statistically consistent with Γ core = 2 . 06 + 0 . 10 -0 . 07 from the 2017 EHT observations.  \nIn addition to the observations taken within the EHT MWL campaign, we also examined data taken with Chandra earlier in 2018: OBSIDs 20488 (4 January 2018) and 20489 (21 March 2018). These observations were processed and analysed in exactly the same way as described above in order to evaluate the core flux using the same set of assumptions. We find that the core flux in the 2-10 keV band was lower in both epochs, ≃ 2 × 10 -12 erg s -1 cm -2 , as shown in Fig. 15.  \nAlthough a PL model provides a satisfactory fit to the April 2018 data alone and is suitable given the sensitivity of the spectrum in a typical single observation, the growing archive of NuSTAR observations of M87 permits a deeper look at the shape of the X-ray spectrum of the core. While the shape of the spectrum appears to be stable, there is evidence of deviation from a PL. In particular, our analysis of 14 existing NuSTAR observations (Sheridan et al., in preparation) reveals statistically significant evidence for a break in the PL around E br = 10 keV: compared to PL fits, models with a break improve the Cash fit statistic by ∆ C > 150 with the addition of only two free parameters. Relative to the pure PL model, the spectrum is softer below the break ( Γ 1) and harder above the break ( Γ 2), though the exact values depend on how the data are combined. For example, when all observations are stacked, we find Γ 1 = 2 . 26 + 0 . 06 -0 . 05 , E br = 11 ± 1 keV, and Γ 2 = 1 . 6 + 0 . 1 -0 . 2 . When the spectral shape is tied between observations but the flux is allowed to vary, we find Γ 1 = 2 . 20 + 0 . 08 -0 . 07 , E br = 12 . 5 ± 1 . 2 keV, and Γ 2 = 0 . 9 ± 0 . 2. We emphasise that Γ 1, E br, and the existence of a break are weakly sensitive to the stacking method, while Γ 2 depends more on the details. In our SED modelling (Sect. 4) we therefore consider all three spectral models for the core. Additional details are included in Appendix B.3.1.", '2.3.2. Swift -XRT observations and analysis': "As a part of the EHT campaign, M87 was observed with Swift -XRTfrom 18 April (MJD 58226) to 29 April 2018 (MJD 58237). A total of 9 observations were conducted in April, with one additional observation on 18 December 2018 (MJD 58240). To put the 2018 observations in context, we also analyse all Swift -XRT photon counting mode observations for most of the Swift -XRT mission duration, extending back to 2009 and forward to 2021. The details of our data processing and spectral analysis are given in Appendix B.3.2.  \nAnalysing these observations constrains both the long-term and short-term variability of the X-ray emission from M87, even though Swift -XRT cannot spatially resolve the inner jet, including the core and the HST-1 knot. The resulting light curve for  \nFig. 6. X-ray spectra of M87 from Chandra and NuSTAR observations in April 2018. Colour-coding for the spatially resolved components is the same as in Fig. 5. The red curves represent the spatially resolved model appropriate for each dataset. The NuSTAR model is the sum of the other spatial components (with cross-normalisation constants to account for instrumental di ff erences between NuSTAR and Chandra as well as between NuSTAR 's two focal plane modules).  \n<!-- image -->  \nFig. 7. Total flux (blue) and unresolved summed component fluxes (red) of M87 in the 2-10 keV band observed with Swift -XRT in 2018.  \n<!-- image -->  \nthe 2018 observations is shown in Fig. 7; we include total unabsorbed flux estimates for M87 with background, as well as net unabsorbed flux estimated by removing the contributions from the background model components. The small-scale components' flux is found to be consistently between 40% and 60% of the total. However, the background-subtracted fluxes shown in Fig. 7 should only be interpreted as upper limits on the X-ray emission from the core, the HST-1 knot, and the jet.", '2.3.3. AstroSat-SXT observations and analysis': 'AstroSat performed two pointing photon counting (PC) mode observations of M87, the first during 17-18 February 2018 (Ob-  \nsID: A04\\_115T02\\_9000001900; MJD 58166.8) and the second during 21-23 April 2018 (ObsID: A04\\_115T02\\_9000002042; MJD 58229.4).  \nWe use a model similar to the one adopted for Chandra and NuSTAR spectral analysis with the addition of a vvapec component to account for the di ff use emission from cluster gas because the SXT samples a larger portion of it. The best-fit normalisation obtained from the SXT spectra is higher than the constraints in M87MWL2017 by about a factor of three mainly for this reason (additional details appear in Appendix B.3.3).', '2.4.1. Fermi -LAT observations': 'Weperformed a dedicated analysis of the Fermi Large Area Telescope (LAT) data (Atwood et al. 2009) of M87 using 12 years of data starting on 4 August 2008, in an energy range between 100 MeV and 1 TeV. We repeated the analysis using a time interval of one month centred on the EHT observation period (i.e. 8 April - 8 May 2018). This choice is also motivated by a problem that a ff ected the solar panel on 16 March 2018, that stopped the observations until 8 April 2018. We summarise these analyses here and include additional details in Appendix B.4.1.  \nThe results from 12 years present a significant excess above the noise level (a test statistic of TS = 2015, corresponding to a significance 3 of > 40 σ with an averaged flux of F = (1 . 74 ± 0 . 11) × 10 -8 ph cm -2 s -1 . The obtained spectral behaviour ( Γ = 2 . 05 ± 0 . 03) is in agreement with the 4FGL-DR2 result for this source (Abdollahi et al. 2020).  \nFig. 8. Fermi -LAT light curve of M87 on customised time intervals during April 2018. The red point refers to the flux estimate, while grey points indicate upper limits. The 1 σ upper limit is reported when TS < 10. The vertical blue line (band) indicates the time of the VHE flaring episode, while the horizontal lines represent the averaged flux over 12 years and its uncertainty F 12 yr = (1 . 74 ± 0 . 11) × 10 -8 ph cm -2 s -1 .  \n<!-- image -->  \nMJD  \nFor the period 8 April - 8 May 2018, the source is detected with a significance of ∼ 6 σ ( TS = 38), F = (5 . 0 ± 1 . 7) × 10 -8 ph cm -2 s -1 , three times higher than the average over 12 years.  \nIt also presents a soft spectrum with Γ = 2 . 40 ± 0 . 21. Regarding the variability study, using for the light-curve study either one-day or two-day cadence temporal analyses, the source was detected (with more than three-sigma significance) in both of the analyses only in the one-day (and two-day) bin around the VHE flare episode. Namely, on the one-day bin centred on MJD = 58228.0 (20 April 2018), presenting a significance of 3 . 5 σ and flux F = (23 ± 10) × 10 -8 ph cm -2 s -1 , as well as in the twoday bin centred on MJD = 58227.5 (19-20 April 2018), with a significance of 3 . 4 σ and flux F = (14 ± 6) × 10 -8 ph cm -2 s -1 , respectively. Similarly, on both the one-week time scale and the customised time intervals (see Fig. 8 and Table C.13) the source is significantly detected (with a TS = 20) only in the week MJD = 58224-58231 (18-24 April 2018), as well as in a four-day period of the VHE flare, namely MJD = 58227-58231, presenting a flux of (8 . 1 ± 2 . 6) × 10 -8 ph cm -2 s -1 and (10 . 8 ± 3 . 8) × 10 -8 ph cm -2 s -1 , respectively. The weekly flux represents the highest weekly emission observed for this source so far. The secondhighest weekly flux ((7 . 2 ± 3 . 1) × 10 -8 ph cm -2 s -1 ) was registered in the first week of February 2014, during which the only VHE observations were obtained with MAGIC. The MAGIC data did not show a VHE flare at this time. Finally, for the four-day period of the VHE γ -ray flare, we performed a spectral analysis of the LAT data finding a PL index of Γ = 2 . 18 ± 0 . 33, compatible with the one measured in the 12-year analysis.', '2.4.2. VHE observations: H.E.S.S., MAGIC, VERITAS': "H.E.S.S. observations in 2018 were performed with the four 12 m CT1-4 telescopes (see Table C.14). A total of 13.0 hours of live time of good-quality data were obtained. The average zenith angle of the observations was 42.9 · . A total statistical significance of 14 . 8 σ was obtained for the 13.0 hours. For the systematic errors we conservatively estimated an uncertainty of 20% on the flux normalisation and 0.1 on the photon spectral index (H.E.S.S. Collaboration 2006b). Detailed information on the H.E.S.S. sensitivity and angular resolution can be found in Parsons & Hinton (2014) and H.E.S.S. Collaboration (2020).  \nMAGIC observed M87 for 11.3 hours after quality cuts for this campaign. The observations were carried out at a mean zenith angle of 29.6 · . The total statistical significance from MAGIC observations during this campaign is 4 . 0 σ . Based on the detailed descriptions in Aleksi'c et al. (2016) we estimate a systematic uncertainty of ∼ 30% on the integral flux that consists of 11% uncertainty of the flux normalisation, ± 0 . 15 on the spectral index and 15% uncertainty of the energy scale. The longterm MWL light curves presented later in Fig. 14 and Fig. 15 also include the MAGIC data collected in 2019. In 2019 MAGIC observed M87 for a total of 29.1 hours after quality selection. The statistical significance obtained from the 2019 observations is 7 . 0 σ .  \nVERITAS (Holder et al. 2006) observed M87 during April 2018 for 10.5 hours after quality cuts with an average zenith angle of about 22.9 · . The total statistical significance from VERITAS observations during this campaign is 7 . 8 σ . We estimated the systematic uncertainty to be ∼ 25% and ± 0.2 on the absolute flux level and spectral index, respectively (Adams et al. 2022).  \nFurther details of the individual analyses can be found in Appendix B.4.2.  \nThe light curves of nightly integrated flux measurements from all VHE instruments above 350 GeV between 10 April 2018 (MJD 58218) and 25 April 2018 (MJD 58233) are shown in Fig. 9 with solid colours. We assumed a PL index of Γ VHE = 2 . 3 for the integrated flux calculations. We chose the same analy-  \nFig. 9. Flux measurements of M87 above 350 GeV with 1 σ uncertainties obtained with H.E.S.S., MAGIC, and VERITAS during the coordinated MWL campaign in 2018. The solid and semi-transparent colours represent the original data and scaled (with cross-normalisation factors) data, respectively. The black curve and shaded region represent the best Gaussian fit and 1 σ error to the original flux points. The grey lines indicate the percentage of the Crab Nebula's flux level from integrating its SED in the same energy range. The SED of the Crab Nebula is obtained by fitting VERITAS's six years of observations (Meagher & VERITAS Collaboration 2015). For flux points with significance less than 2 σ , upper limits are given in Table C.14.  \n<!-- image -->  \nis threshold of 350 GeV as for our 2017 campaign (EHT MWL Science Working Group et al. 2021). Strong moonlight conditions prevented observations after 25 April 2018. In order to investigate possible flux variability, we fitted the flux measurements from all VHE telescopes with a constant flux model. This yields a χ 2 / d.o.f. of 42.61 / 16 corresponding to a probability of p ≈ 3 × 10 -4 . In order to estimate the variability time scale, we fitted the light curve with a Gaussian function:  \nF ( t ) = F 0 + F 1 · exp -( t -t 0) 2 2 σ 2 ! , (1)  \nwhere F 0 was added as baseline flux. The Gaussian fit provides a better description of the measured data with χ 2 / d.o.f. of 16.46 / 13 corresponding to a probability of p ≈ 0 . 23. The best fit results are F 0 = (1 . 25 ± 0 . 46) × 10 -12 ph cm -2 s -1 , F 1 = (2 . 83 ± 0 . 62) × 10 -12 ph cm -2 s -1 , t 0 = 58229 . 07 ± 0 . 39 MJD (21 April 2018), σ = 1 . 32 ± 0 . 44 days. Defining the variability time scale as the full width at half maximum (FWHM), we obtain FWHM = 2 √ 2 ln 2 σ ≈ 2 . 35 σ = 3 . 10 ± 1 . 03 days. For comparative purposes, the rising part of light curve was also fitted with an exponential function of the form  \nF ( t ) = p 0 e -| t -58226 | /τ , (2)  \nwhere the exponential time scale τ is 1 . 88 ± 0 . 27 days for the rising flux portion. Furthermore, a Gaussian function fit to H.E.S.S. data alone yields a χ 2 / d.o.f. of 0.44 / 2 corresponding to a probability of p ≈ 0 . 80, F 0 = (0 . 77 ± 1 . 06) × 10 -12 ph cm -2 s -1 , F 1 = (4 . 50 ± 0 . 95) × 10 -12 ph cm -2 s -1 , t 0 = 58229 . 15 ± 0 . 11 MJD, and σ = 1 . 28 ± 0 . 34 days.  \nLight curves with shorter time binning of less than one night were calculated, but no significant intra-night variations were found. Further discussion of possible systematic crossnormalisation factors between IACTs and how the factors a ff ect the fitting results can be found in B.4.2. To conclude, the statement that a model with a Gaussian peak describes the light curve significantly better than a constant holds even taking potential systematic cross-calibration o ff sets between the VHE instruments into account. The cross-normalisation factors provide a  \npossible range of fitting parameter values and reduce the tension of the flux di ff erence between VERITAS and H.E.S.S. around MJD 58229 (semi-transparent colours in Fig. 9). However, for this research, there is no compelling reason for a rescaling of the original data, rea ffi rming the fact that the fit to the flare profile without scaling is acceptable.  \nFig. 10. Very high-energy SEDs measured with H.E.S.S., MAGIC, and VERITAS during the coordinated MWL campaign in 2018. Error bars are equivalent to the 1 σ confidence level. An upper limit is drawn where the statistical significance is less than 2 σ (i.e. 95% confidence level). The thick solid lines represent the best spectral fitting results by using a simple PL function. The shaded regions represent the 1 σ error region of the fitting results. Historical spectral fits are taken from H.E.S.S. Collaboration (2006a,b); Aleksi'c et al. (2012); Acciari et al. (2008, 2010); Albert et al. (2008); Aliu et al. (2012); Beilicke & VERITAS Collaboration (2012); MAGIC Collaboration et al. (2020) and the thin black line indicates the SED obtained with the MAGIC measurements presented in M87 MWL2017.  \n<!-- image -->  \nIndividual SEDs obtained with all VHE instruments are shown in Fig. 10 and results of the spectral fits are given in Table C.15. The photon spectra are described with a simple PL function:  \nd N / d E = f 0 · E E 0 ! -Γ VHE , (3)  \nwhere f 0 is the normalisation at the energy E 0 and Γ VHE is the photon spectral index. It is important to note that the three VHE SEDs are results of di ff erent samplings during the observational campaign. MAGIC observed primarily during nights before and after the VHE flux rise discussed below. About half of the VERITAS data were taken before and the other half during the flare. The H.E.S.S. observations cover the whole time span of the flare and lead to the hardest spectral index amongst the three instruments (Fig. 10). This result points to a harder-when-brighter behaviour in VHE, as previously hinted in H.E.S.S. Collaboration (2006a); Albert et al. (2008); Acciari et al. (2010); Aliu et al. (2012); H.E.S.S. Collaboration (2023). We acknowledge that directly comparing the spectral slopes of H.E.S.S., MAGIC, and VERITAS is also limited by the di ff erence in the energy ranges covered by their respective spectra. Di ff erently than the results  \nFig. 11. Composite of the M87 MWL images at various scales obtained in radio and X-rays during the 2018 campaign. The instrument, observing wavelength, and scale are shown at the top-left side of each image. We note that the colour-scale has been chosen to highlight the observed features for each scale and should not be used for noise levels, dynamic range, or flux density calculation purposes.  \n<!-- image -->  \nfrom H.E.S.S. Collaboration (2024), the spectrum derived from the H.E.S.S. data in this study is limited to a maximum energy of 6.9 TeV. This limitation ensures that each energy bin in Fig. 10 is detected with a significance of at least 2 σ .", '3.1. Multi-wavelength images in 2018': 'In Fig. 11, we show a composite of M87 MWL images obtained by radio and X-ray instruments during the 2018 campaign, including the EHT image. The M87 jet is imaged at multiple scales from ∼ 1 kpc down to a few Schwarzschild radii (defined as r s = 2 GM BH / c 2 = 2 r g where M BH is the mass of M87*, G the gravitational constant, c the speed of light, and r g is the gravitational radius) summarising a contemporaneous MWL view of M87 during the 2018 EHT campaign.  \nTo check the structural variations, we compared the data obtained in 2017 and 2018. While the appearance of the kpcscale jet remained relatively consistent during this period, our high resolution VLBI observations unveiled some morphological evolution in the pc-scale jet between the two years (see Fig. 12). Notably, these changes encompassed a counterclock-  \nFig. 12. M87 jet structure comparison based on yearly stacked EAVN images at 43 GHz in 2017 and 2018 (see also Cui et al. 2023). The restoring beam size is 0.5 mas circular Gaussian, as indicated in the bottom-left corner. The image has been rotated by -18 · . Contour levels are scaled as (1, 2, 4...) × C 1st, where C 1st is 1.8 mJy beam -1 in 2017 and 1.3 mJy beam -1 in 2018. For M87* mass M BH = 6 . 5 × 10 9 M ⊙ (Event Horizon Telescope Collaboration et al. 2019a), 1 mas ≈ 125 rs ≈ 0 . 08 pc, where r s is the Schwarzschild radius.  \n<!-- image -->  \nwise shift in the jet position angle (PA), occurring within a few milli-arcsecondfrom the core (PA ∼ 281 · / 283 · in 2017 / 2018; see Cui et al. (2023) for details). Furthermore, the data from 2018 reveal a more symmetric brightness ratio of the north-south jet limbs. All of these variations suggest the existence of year-scale structural evolution transverse to the jet. Moreover, regular VLBI monitoring observations reveal a gradual decrease in core flux (by 10-15%) at 22-129 GHz from 2017 to 2018 as shown in Fig. 2. Closer to the SMBH, GMVA 86 GHz observations in concert with ALMA and GLT spatially resolve the radio core, revealing a ring-like structure connecting to an edge-brightened jet (Lu et al. 2023).  \nContemporaneous observations obtained with the VLBA at 24 and 43 GHz were used for the spectral analysis (Sect. 4). Figure 13 compares maps of the spectral index ( α ; defined as F ν ∝ ν α ) between these two frequencies from observations on 5 May 2017 (panel (a)) and 28 April 2018 (panel (b)). The M87 jet has an edge-brightened structure and does not have distinct components at each frequency to anchor an alignment of different images. We model-fitted the jet structure by a number of two-dimensional Gaussian components, aligned images at the position of the 43 GHz core, and shifted 24 GHz images by the value of the core shift. Finally we measured the shifts from the phase referencing observations, that are 0.036 and 0.012 mas in right ascension and declination, according to Hada et al. (2011) and Ro et al. (2023). Even though the amplitude of the core shift may change with time (Plavin et al. 2019), multiple studies of M87 indicate the magnitude of the core-shift variation in 22-43 GHz is within 10 µ as and negligible (Hada et al. 2012, 2014; Jiang et al. 2021). The core regions in both 2017 and 2018 show relatively flatter spectral index values ( α ≈ 0) than jet regions. However, compared to 2017 observations (Fig. 13(a); M87MWL2017), the value of the spectral index in 2018 steepens faster downstream from the core region to the jet region. It  \n<!-- image -->  \nFig. 13. Spectral index maps obtained between 24 and 43 GHz from VLBA 5 May 2017 (a) and 28 April 2018 (b) observations. The restoring beam size is 0.55 mas × 0.55 mas, that is the circular equivalent-area synthesised beam of VLBA at 24 GHz, drawn as a grey circle in the bottom-right corner. The image has been rotated by -18 · . The contours denote the total intensity at 43 GHz and start at 3 . 5 σ rms, increasing in steps of two. The ( u , v )-ranges match between all images.  \n<!-- image -->  \nindicates that the radio emission in the jet close to the core becomes relatively stronger at lower frequencies compared to the emission in the core in 2017, that might be related to changes in the physical conditions of the particle accelerator at the core. In the extended jet regions, the spectral index distribution appears to be more complex: it shows flatter values ( α > -0 . 5) at the location of total intensity maxima with steeper values ( α < -1) that are localised in between. The M87 jet structure and spectral index distribution based on the 8-15 GHz VLBA observations with the highest-to-date dynamic range (Nikonov et al. 2023) are consistent with the development of Kelvin-Helmholtz instabilities in the jet in regions of 10 2 -10 4 r g from the central engine. Their propagation can produce higher pressure regions near the jet boundary and in its interior, causing variations of the spectral flattening towards the jet edges, as shown in Fig. 13. The observed variability in flat- and steep-spectrum regions could also be influenced by the jet precession, that modulates the observed jet structure over time. However, for the variability results obtained here, rescaling of the original data is not crucial, reaffirming the fact that such a complicated spectral profile in the edge-brightened jet region could be (at least) partly a ff ected by insu ffi cient interferometric (u,v) coverage (e.g. Pashchenko et al. 2023).', '3.2. Change in the mas-scale jet and µ as-scale ring asymmetry position angles': 'As reported in Sect. 3.1, a slight and counterclockwise variation in the PA of the mas-scale jet was detected with 43 GHz VLBI observations in 2017 and 2018. The observed PA evolution from 2017 to 2018 appears to follow the long-term (quasi-)periodic variation (on time scales of ∼ 10 years) found by the recent VLBI  \njet monitoring studies (Walker et al. 2018; Cui et al. 2023). Possible scenarios causing such year-scale variations of the jet PA include MHD / HDinstabilities (Mizuno et al. 2007; Walker et al. 2018) and Lense-Thirring precession in a tilted accretion disk system (Fragile et al. 2007; Liska et al. 2018; Cui et al. 2023). The observed variation in the north-south limb brightness ratio could also be related to the jet PA variation and / or transient turbulence in the jet flow, which requires further dedicated investigations.  \nSimilarly, the EHT-2018 images (M87 2018 I) reveal a significant shift of the PA of the brightness asymmetry of the ring with respect to that of EHT-2017 images (presenting a counterclockwise variation ∼ 30 · from 2017 to 2018). The shift direction is as expected if the large-scale jet is aligned with the disk rotation axis (M87 2017 V). Morphological variations of the asymmetric ring at EHT scales could be related to the week-scale variation caused by magnetorotational instabilities (M872017V, M872018I). On the other hand, the observed PA change in the EHT images between 2017 and 2018 may imply the presence of a longer-term (year-scale) variation at this scale as originally suggested by Wielgus & Event Horizon Telescope Collaboration (2020).  \nNevertheless, it is worth noting that M87 shows counterclockwise PA variations at both milli-arcsecond and microarcsecond scales between the two years, that sheds light on the possible physical connection between the mas-scale jet and µ as-scale ring. The recent detection of both the ring-like structure and extended jet in a single image using global VLBI at 86 GHz o ff ers a new potential to explore this challenge by bridging the spatial gap between µ as- and mas-scale structures (Lu et al. 2023). Future multi-year comparisons of multi-frequency (230 / 86 / 43 GHz) VLBI images will be the key to constraining the origin of variable structures at di ff erent scales and their physical connection.', '3.3. Multi-wavelength light curve and fractional variability': "In Figs. 14, 15, and 16 we show the long-term (2000-2022), midterm (2017-2019) and short-term (April 2018) MWL light curve behaviour of M87 with the observations collected by the instruments participating in the coordinated MWL campaign in 2018.  \nComparing the 2018 EHT-MWL campaign (Fig. 16) to 2017 (M87MWL2017), the source presents brighter emission at high energies, seen both in the X-ray as well as in the γ -ray energy bands, while in optical and at radio frequencies the core flux remained the same or even a bit dimmer. In particular, Chandra observations during April 2018 revealed a core flux increase of ∼ 80% with respect to the previous 2017 campaign. While the duration of the VHE γ -ray flaring episode is well constrained (see Sect. 2.4.2 and further discussion in Sect. 3.3.1), the lack of monitoring X-ray observations of the source does not allow us to fully investigate the duration of the X-ray variability, as we further elaborate in Sect. 3.3.2.  \nThe fractional variability (Vaughan et al. 2003) of the VHE γ -ray band in 2017 is compatible with zero within uncertainties. The same applies to the 2018 VHE γ -ray variability. This is caused by the large statistical uncertainties of the VHE data. We note that these large statistical uncertainties are also limiting the calculation of a discrete correlation function (Edelson & Krolik 1988). The fractional variability of the Swift -XRT flux of the small-scale components (namely the core, the HST-1 knot and inner jet) is significantly lower in 2018 than in 2017. For both years these fractional variabilities are slightly higher than  \nFig. 14. Long-term MWL light curves of M87 from the last two decades taken by the instruments participating in the 2018 observational campaign. Here are listed the observations used in the plot and their reference (in case of already published results): Chandra (0.3-7 keV) (Sun et al. 2018), (2.0-10 keV) (Imazawa et al. 2021); MAGIC, H.E.S.S., VERITAS (2004-2011), Fermi -LAT (2008-2011), VLBA (43 GHz peak) (2006-2011) (Abramowski et al. 2012); H.E.S.S. (before April 2004, 2001-2016, after 2019) (H.E.S.S. Collaboration 2023); VERITAS (2011-2012) (Beilicke &VERITASCollaboration 2012); VERA (2011-2012), MAGIC (2012-2015) (MAGIC Collaboration et al. 2020); KVN (2012-2016) recalculated from (Kim et al. 2018); VLBA, EAVN, VERA (2000-2018) (Cui et al. 2023); All instruments 2017 data (M87 MWL2017). We mark the VHE flare in 2018 with a grey-shaded line in the background. The inset in the third row provides a zoomed-in view of the Chandra measurement of HST-1.  \n<!-- image -->  \n1  \n2  \n1  \n1  \nDate  \nMJD  \nFig. 15. Mid-term MWL light curves of M87 taken during the observational campaign covering January 2017 to April 2019. Here are listed the observations used in the plot and their reference (in case of already published results): Chandra (0.3-7 keV) (Sun et al. 2018), (2.0-10 keV) (Imazawa et al. 2021) H.E.S.S. (2019) (H.E.S.S. Collaboration 2023); VLBA, EAVN, VERA (2017-2018) (Cui et al. 2023); All instruments 2017 data (M87 MWL2017). We mark the VHE flare in 2018 with a grey-shaded region in the background.  \n<!-- image -->  \nin the VHE γ -ray band, although well compatible within statistical uncertainties. Further information on our calculation of the fractional variability are reoperted in Appendix D. The resulting fractional variability values are given in Table D.1.  \nWe combined the spectral slopes and flux normalisations from the three VHE energy spectra obtained in this campaign  \nDate  \nFig. 16. Multi-wavelength light curves of M87 taken during the observational campaign covering MJD range 58211--58245. The time period containing the 2008 VHE flare is shaded in grey.  \n<!-- image -->  \nMJD  \nwith the historical measurements to test whether there is a general harder-when-brighter behaviour in the VHE band. The photon indices are plotted against the flux normalisations in Fig. 17,  \nin which it is possible to see that there is no dependence of the spectral index on the flux. However, this comparison is limited by the fact that the VHE spectra shown cover di ff erent energy  \nFig. 17. Photon PL index Γ compared to the VHE flux normalisation at 1 TeV. Data from H.E.S.S. Collaboration (2006a); Acciari et al. (2008); Albert et al. (2008); Acciari et al. (2010); Aliu et al. (2012); Aleksi'c et al. (2012); Beilicke & VERITAS Collaboration (2012); MAGIC Collaboration et al. (2020); EHT MWL Science Working Group et al. (2021). Flux normalisations at di ff erent energies have been recalculated at 1 TeV using the respective power-law index. No dependence of the spectral index on the flux was observed.  \n<!-- image -->  \nranges (H.E.S.S. Collaboration 2006a; Acciari et al. 2008; Albert et al. 2008; Acciari et al. 2010; Aliu et al. 2012; Aleksi'c et al. 2012; Beilicke & VERITAS Collaboration 2012; MAGIC Collaboration et al. 2020; EHT MWL Science Working Group et al. 2021).", '3.3.1. VHE gamma ray flaring episode': "The first strong indication of VHE γ -ray emission from M87 was found in 1998 with the High Energy Gamma Ray Astronomy (HEGRA) telescopes (Aharonian et al. 2003). Since 2004, frequent monitoring (Acciari et al. 2009; Abramowski et al. 2012) of M87 has been conducted by H.E.S.S. (H.E.S.S. Collaboration 2006a), MAGIC (Albert et al. 2008; Aleksi'c et al. 2012; MAGIC Collaboration et al. 2020), and VERITAS (Acciari et al. 2008, 2010; Aliu et al. 2012; Beilicke & VERITAS Collaboration 2012). These monitoring campaigns have yielded detections of VHE flaring activity from M87 in 2005 (H.E.S.S. Collaboration 2006a), 2008 (Albert et al. 2008), and 2010 (Abramowski et al. 2012). Furthermore, while VERITAS observations carried out in 2011 and 2012 did not show bright flares, they reported a hint of an underlying quiescent increase of the VHE emission evolving on longer time scales than day-scale rapid flares (Beilicke & VERITAS Collaboration 2012).  \nDespite deep investigations of past flaring episodes of the source as well as of the quiescent state during the 2017 EHT campaign (M87 MWL2017), the origin of the VHE γ -ray emission from M87 is still unclear. A unique opportunity to address this question is provided by the correlated temporal variations between the VHE and lower-energy MWL emission, particularly when the source can be spatially resolved (Rieger & Aharonian 2012; Rieger & Levinson 2018). Fortuitously, as shown in Sec. 2.4.2, the 2018 MWL campaign detected the first fast γ -ray variability observed since 2010, providing a chance to investigate the origin of the γ -ray emission in M87.  \nDuring the 2018 MWL campaign the VHE flux increased by a factor ∼ 3.3 with respect to April 2018 low state (see Sect. 2.4.2, Fig. 9) reaching a peak flux that is still far below the flares observed in the past but a factor ∼ 4 higher than the quiescent state flux measured during the 2017 campaign  \n(M87MWL2017). In 2005 the peak flux at energies > 350 GeV reached (1 . 41 ± 0 . 39) × 10 -11 ph cm -2 s -1 , almost 20 times the quiescent state flux as measured during the 2017 EHT-MWL campaign (M87 MWL2017). Similarly, in 2008 and in 2010, the observed fluxes were 23 and 37 times the quiescent state flux (see Fig. 14), respectively.  \nThe variability time scales observed during the past VHE flares are similar to the 2018 flare (FWHM approximately three days) presented here, albeit somewhat faster. The 2005 and 2008 flares showed a roughly two-day and one-day variability, respectively (H.E.S.S. Collaboration 2006a; Albert et al. 2008). In 2010 a two-sided exponential fit revealed a rise time of 1 . 69 ± 0 . 30 days and a decay time of 0 . 611 ± 0 . 080 days (Abramowski et al. 2012). The general light curve shape of the 2018 flare is similar to the flare seen in 2010, with only one isolated peak, while the flares observed in 2005 and 2008 comprise multiple separated high states during a similar time period.", '3.3.2. Flux enhancement in the X-ray band': "The X-ray flux from the unresolved core as observed with Chandra (within 0.4 arcsec radius), the combined core, HST-1, and jet fluxes from Swift -XRT, and the Swift-UVOT B- and U-band fluxes are all generally higher in April 2018 compared to both 2017 and 2019, as shown in Fig. 15. However, without additional X-ray / UV / optical observations immediately before and / or after the gamma-ray flare period, it is di ffi cult to establish a firm link between the elevated X-ray / UV / optical fluxes and the flare itself. Figure 14 shows that M87's core X-ray flux can vary to a similar degree even in the absence of a flare, so the elevated X-ray / UV / optical flux near the 2018 flare cannot be definitively linked to the gamma-ray flare itself.  \nIt is, anyway, tempting to relate the observed X-ray flux enhancement to the flaring observed in the VHE band despite the very sparse time coverage of Chandra observations. The di ff erence in core flux between the two Chandra observations during the EHT 2018 campaign is clear but small ( ≃ 20 %; see Fig. 5) and rising away from the VHE flare that peaked approximately one day and three days before the first and the second Chandra observations, respectively. Two AstroSat-SXT observations, that do not directly resolve the core, suggest a modest increase in flux between February and April 2018.  \nDenser sampling in time is a ff orded by our Swift -XRT observations (Fig. 7), providing a measure of variability in the joint flux of the small-scale components (core, the HST-1 knot, and the resolved jet), that cannot be resolved individually. This flux, modelled as a fraction of the total flux observed by Swift -XRT does not show any coherent flux increases comparable to the VHE flare within the ≃ 11-day campaign window covered with roughly daily cadence. The variance over this period (variability amplitude of 5-10%) is consistent with that of long-term Swift -XRT monitoring. Comparing the Swift -XRT small-scale component fluxes to the Chandra core flux, we find that the Swift -XRT small-scale components fluxes produced by our analysis are typically about double the core fluxes reported in Table 1 of Cheng et al. (2023). Our Swift -XRT small-scale component fluxes and the Chandra core fluxes show a similar trend, presenting a small flux increase between 22 and 24 April 2018.  \nA model-dependent way to test for an association between the X-ray brightening and the VHE flare would be to search for changes in the X-ray spectrum. For example, a harder X-ray spectrum than observed in non-flaring states (e.g., the 2017 EHT campaign) might be expected in some broadband SED models (discussed in detail in Sect. 4.3). While Cheng et al. (2023) claim  \nto find hardening in Chandra observations from April 2018, we find no evidence for this e ff ect in our joint spectral analysis with NuSTAR (possibly because we account for pileup and use a broader energy range to measure the photon index). In our power law model, we find Γ core = 2 . 03 + 0 . 12 -0 . 07 in 2018, statistically consistent with Γ core = 2 . 06 + 0 . 10 -0 . 07 from the 2017 EHT observations. Thus the only statistically significant change in the X-ray core is the flux increase described above. Finally, we note that this flux enhancement is insensitive to the X-ray spectral model: because the PL break lies above 10 keV, it does not dramatically alter the inferred 2-10 keV core flux.", '3.4. Multi-wavelength SED': "As discussed in the previous subsections, the source underwent a γ -ray flaring episode during the 2018 EHT-MWL campaign, with a hint of spectral hardening towards the highest TeV energies. While a moderate flux enhancement is also seen at X-ray energies with respect to 2017 EHT-MWL observations (M87MWL2017), in the radio band, the source presents a comparable, or even lower, emission. These features can be seen in Fig. 18 that shows the 2018 MWL broadband SED (reported also in Table C.1), where the 2017 EHT-MWL campaign SED is shown in the background in grey for comparison. The fact that the source was in a low state at all frequencies while the HST-1 knot emission was subdominant to the core in the X-ray band allowed us to confidently associate the Chandra and NuSTAR total fluxes with the core emission in 2017 data; however, the observed X-ray and VHE variability seen in 2018 leads to uncertainties regarding their localisation.  \nIn Table C.1 we present the legacy data set with nearsimultaneous broadband SED results collected over a broad range between a frequency of ≃ 20 GHz and photon energy ≃ 1 TeV. Similar to M87 MWL2017, we report for each given energy or frequency range the associated spatial scale of the total emission, the time ranges of the observations and fluxes (providing both F ν and ν F ν ). As described in Sect. 2, all flux points are based on statistically significant detections ( > 3 σ for all instruments except > 2 σ for IACTs) and provided with uncertainties equivalent to the 1 σ confidence level. Upper limits on flux are given at the 2 σ confidence level. The angular scale of the emission measured by the di ff erent instruments involved in this campaign, ranges between ≃ 20 µ as (EHT) and ≃ 2 · ( Fermi -LAT in low-energy γ -rays), namely a factor ≳ 10 8 in angular scale.  \nBesides the EHT observation (for which we list the estimated total flux within a 60 × 60 µ as 2 region as described in Event Horizon Telescope Collaboration et al. 2019d), for other radio observations if the core is not resolved we use the flux peak obtained from the beam size as an upper limit on the core emission. For elliptical beams, we list the average of the axes as a representative angular scale. For the observations carried out in the optical and UV bands, due to the di ff erence between Kanata and Swift-UVOT angular resolution ( ∼ 10 '' and ∼ 2.5 '' , respectively), we consider the first as an UL and the second as flux points, similarly to what was done for the Swift-UVOT observation carried out in M87 MWL2017. This di ff erence is related to the larger uncertainties in the subtraction of the host galaxy contribution in the Kanata optical observations. However, we want to highlight that, considering its angular resolution, Swift-UVOT measurements include both the core and the HST-1 knot contribution. At higher energies, the scale typically corresponds to the point spread function (PSF) within a given band or the diameter of the region used for data extraction. For Chandra and NuS-  \nTAR , as in EHT MWL Science Working Group et al. (2021), we show the inferred core spectrum from the power-law and broken power-law models described in Sect. 2.3.1. We discuss the implications of the inferred X-ray spectrum on the SED modelling in Sect. 4.Despite the fact that NuSTAR can only separate out the core emission spectroscopically (see Sect. 2.3.1), the reported scale for Chandra and NuSTAR data analysed jointly is related to Chandra 's greater resolving power. Given that Swift -XRT cannot distinguish the core from other components detected by Chandra , we treat its unresolved flux as an upper limit.  \nConsidering the timing relative to the observed γ -ray flare it is important to note that the majority of the observations arranged during our 2018 campaign have been carried out either during or within a few days of the flaring episode, well within the minimum dynamical timescale (5 - 61 days; Satapathy et al. 2022) for the innermost stable circular orbit of M87. The only exception is represented by global 43 GHz VLBI observations, collected in February 2018, that have been included taking into account the non-variability seen in the radio band on short and long time scales (see Sect. 3.3). Although these observations were not all strictly simultaneous with the γ -ray flare and EHT observations, the non-variability observed for the radio, optical and X-ray bands, suggests that they e ff ectively provide a snapshot of the broadband SED of the M87 core. These results can be used as constraints for any model seeking to explain the source behaviour around the time of the observed γ -ray flare and EHT 2018 image acquisition.  \nIn addition to highlighting the various observatories and instruments with di ff erent colours and labels, we also report the upper limits on the size of the emission region for the VLBI measurements. These data are provided to the community in machine-readable form via the EHTC Data Webpage 4 (or directly via DOI «TBD») along with associated documentation and files needed for spectral modelling of the X-ray data. A complete list of all supplementary material published along with this paper is provided in Appendix C.", '4. Heuristic modelling of the 2018 SED': 'A variety of theoretical models have been proposed to explain MWLemission from M87 (e.g., Rieger & Aharonian 2012; Prieto et al. 2016, for review). Given the M87 polarisation imaging constraints (Event Horizon Telescope Collaboration et al. 2021c,d), the innermost, ionised regions must be moving along strong field lines. While the accretion flow could contribute in some parts of the SED, the jet radiation is more likely to be dominant in the SED as a whole (e.g., EHT MWL Science Working Group et al. 2021, and references therein). For this work, we choose to model the SED based on single zones, a common approach to many AGN blazar studies; we defer more sophisticated modelling such as structured jet models (e.g., Potter & Cotter 2013; Lucchini et al. 2019) to future works. Models based on single zones enable the baseline properties of the emitting regions to be constrained. In addition, this approach allows for comparisons to other M87 epochs, including the 2017 campaign, as well as to the other AGN observed by the EHT.  \nThe origin of the VHE γ -ray emission from M87 is still unclear. The MWL observation campaign described above (see Sect. 2) o ff ers a unique opportunity to tackle this issue, given the following key points: (i) the duration of the VHE γ -ray flaring episode is well constrained, while we cannot detect similar ac-  \nFig. 18. Observed broadband SED of M87 contemporaneous with the EHT campaign in April 2018, with the exception of GMVA (carried on February 2018) and HST (carried on July 2018) observations (see Table C.1). The fluxes were measured by various instruments, and they are highlighted with di ff erent colours and markers. Filled markers represent flux point estimates, while empty markers indicates ULs. In the X-ray band, the two sets of points represent the core flux from the models with a power law (dark purple) and broken power law (light purple; see Table C.12). Grey points represent the observed broadband SED of the 2017 EHT campaign (EHT MWL Science Working Group et al. 2021). MAGIC observations were performed before the γ -ray flare, VERITAS observations only partially overlap the flare, while all H.E.S.S. observations have been taken during the VHE γ -ray flaring episode (see Table C.1). For the radio / millimetre VLBI and interferometric data, the upper limits on emission size (radius) for several representative frequencies, that are obtained by following the procedures described in M87 MWL2017, are labelled to clarify the constraints used in Sect. 4.  \n<!-- image -->  \nvity in the other energy bands, and (ii) EHT spatially resolved the photon ring and measured its flux during the flaring episode. With these data, the origin of the VHE γ -ray emission from M87 can in principle be better constrained. We briefly describe a heuristic model to explore this. First, as in M87 MWL2017, we introduce the EHT-oriented model. This model uses the flux density and the size of the emitting region obtained from actual EHT observations and allowed us to constrain the average physical state of the EHT emitting region near the black hole, assuming it is uniform within the region. Within the context of this approach, we find that the high energy γ -ray emission cannot be explained only by the EHT-oriented model. Therefore, the portion of the radiation that cannot be explained by the EHT-oriented model will be explained using a high-energy(HE)-oriented model, whose size is set by the VHE γ -ray flare duration.  \nEven with the unique 2018 observational data, simple HEoriented models are degenerate in interpretation. We therefore present two model scenarios (Model A and B), both of which assume a TeV γ -ray flux increase during the flare. Model A is a more straightforward two-zone model that is a combination of the EHT-oriented model and the HE-oriented model. It is char-  \ncterised by having mildly relativistic bulk flow and an internal emitting particle distribution (assumed leptonic) to explain the observed TeV γ -ray data. In contrast, Model B is a three-zone model with an additional fast-moving component (corresponding to a part of the accelerating jet) that explicitly accounts for the Γ VHE ≈ 2.0 component of the TeV γ -ray data. In this scenario, the γ -ray emission originates from the fast-moving component, as often proposed in the literature (e.g., Ghisellini et al. 2005; Giannios et al. 2009, and references therein).  \nHere the shape of the X-ray spectrum also becomes relevant. As discussed in 2.3.1 (see also Sheridan et al., in preparation), a stacked analysis of NuSTAR observations of M87 including the 2018 data suggests a break in the X-ray PL spectrum that (i) appears to be stable from year to year and (ii) bends upward towards the γ -ray band. Extrapolating this broken power-law (BPL) to higher energies results in a substantially larger MeV energy flux compared to the decaying PL model. Because the 2018 data are statistically consistent with both the PL model and the BPL model, in subsequent sections we consider both scenarios when modelling the full SED with Model A (see Table 1, Sect. 4.4, and Fig. 21). We are left with three separate models  \nexplored in the sections below: model-A with PL, model-A with BPL, and model-B with PL. Unfortunately, the most notable difference between the PL-based models and BPL-based models is a large MeV bump that is not sampled by our observing campaign (see, e.g., Fig. 21), and so ultimately we are not able to rule out either X-ray model based on SED considerations.  \nThe SED models include parameters relating to the source bulk physical properties as well as the internal emitting particle properties. The microphysics regarding the acceleration of leptons is phenomenologically included in non-thermal electron distributions (hereafter we refer for ease to these as electron distribution functions; eDFs). For this paper, GR and γ -γ absorption e ff ects are not considered in order to enable comparisons with the models in M87 MWL2017 and other single-zone models. In fact, H.E.S.S. Collaboration (2024) demonstrate that the absorption caused by the Extra-galactic Background Light (EBL) only becomes notable ( τ EBL > 0.2) in M87 for γ -ray energies exceeding 10 TeV. Additionally, the absorption attributable to the infrared light from the galaxy is also found to be negligible ( τ IR < 0.001) for TeV γ -rays. Lastly, the absorption by photons in the vicinity of the SMBH at 10-50 rg , where rg = GM / c 2 is the gravitational radius of the black hole of M87*, might have a more significant impact (Brodatzki et al. 2011; Cui et al. 2012). We have assessed the influence of synchrotron self-absorption (SSA) on the energy flux of TeV photons, utilising the best-fit parameters from our models (see Sect. 4.3). Our analysis indicates that SSA, at most, a ff ects the energy flux by less than an order of magnitude. Consequently, this phenomenon does not significantly a ff ect the interpretation of the findings presented in this study. However, we acknowledge that the exact locations of the individual zones proposed in our models relative to each other are unclear and could potentially impact the overall SED. Due to the complexity involved, this aspect remains unexplored in the current work for the sake of simplicity.', '4.1. Observed differences between 2017 and 2018 source emission influencing the SED modelling': 'Before we begin to explain the SED modelling in detail, it is worth summarising the di ff erences between the 2017 and 2018 source emission that influence the SED modelling. The most notable di ff erences are the flux increase and a hint of a spectral hardening ( Γ VHE ≈ 2.0) observed in the VHE γ -ray energy band during the April 2018 campaign. As reported in Sect. 2.4.2, the FWHMtime scale of the VHE flux variation was ∼ 3 days, providing an important constraint on the size of the γ -ray emitting region in April 2018 that was not available in 2017, albeit degenerate with relativistic beaming e ff ects.  \nOur interpretation of the X-ray data for the modelling also di ff ers somewhat compared to M87 MWL2017. While some variability is implied by the flux enhancement in the X-ray band in 2018 in comparison with 2017 (see Fig. 15 as well as Sect. 3.3.2), there is no definitive evidence of its association with the VHE γ -ray flare. Therefore, we will examine both of the cases where the X-ray flux is associated with the EHT-oriented model and high-energy ( E > 100 MeV) oriented model.  \nFinally, we treat the optical / IR flux observations as upper limits because we cannot separate out the galaxy contamination due to the inadequate resolution of the Kanata optical telescope. While we choose a value of the SED parameters to keep the model within the range consistent with the Swift -UVOT emission.', '4.2. EHT oriented model': 'The following free parameters characterise the spherical, singlezone emission region: the radius ( R ), the Doppler factor of the bulk motion ( δ ), and the averaged magnetic field strength ( B ). The Doppler factor is defined as δ = 1 / Γ L (1 -β cos θ view), where Γ L is the bulk Lorentz factor of the emission region, and we set θ view = 17 · (Event Horizon Telescope Collaboration et al. 2019a). The nonthermal eDF within the emission region is characterised by the electron number density ( ne ), the spectral indices of the broken power law ( p 1 and p 2), and the minimum / break / maximum Lorentz factors ( γ min, γ brk, and γ max), respectively.  \nOverall, the characteristics of the radio-to-millimetre emission in the observed broadband SED in April 2018 are similar to the SED in April 2017 (M87 MWL2017). Therefore, we followed a similar modelling methodology. First, we set R to be equivalent to the photon-ring size (Event Horizon Telescope Collaboration et al. 2019a), that is, the single-zone e ff ectively encompasses a sphere around the SMBH out to R ≈ 5 r g. Since the bulk speed of the jet has not yet become relativistic, we fix δ ∼ 1. We set γ min = 1 as it has little influence on the SED, that is, the value of γ min a ff ects the energy density of nonthermal electrons Ue only if the photon index is softer than 2 (e.g., Kino et al. 2002; Finke et al. 2008). Therefore, we fixed the conservative γ min for simplicity. Based on observed radio core shift measurements (Hada et al. 2011), the frequencies below ∼ 230 GHz are optically thick to synchrotron self-absorption (SSA), that constrains the magnetic field strength B > a few G (Event Horizon Telescope Collaboration et al. 2021d). Keeping this limit in mind, one can fit the SED by only adjusting B and the eDF parameters. The best fit model parameters are summarised in Table 1. There are no essential di ff erences between 2017 and 2018 for the EHT-oriented model.  \nFig. 19. Model A fitted to the observed contemporaneous broadband SED of M87 from the EHT campaign in April 2018. For each considered region (EHT and HE), the synchrotron (labeled as SYN) and synchrotron self-Compton (labeled as SSC) models as well as the summation of the two models (labeled as Sum) are reported.  \n<!-- image -->  \nSimilar to the results obtained in M87 MWL2017, we find that the entire MWL radiation cannot be explained by the EHToriented model alone. In this model Ue and UB are approximately comparable in the EHT emission region when γ min = 1. Since we adopt γ min = 1, Ue provides an upper limit. Although a  \ndetailed parameter survey will be left to future studies, UB > Ue is anticipated for γ min > 1 in the EHT emission region. This would align more closely with the scenario of a magnetically accelerated jet.', '4.3. High-energy oriented model': "Here we introduce the HE-oriented model to fit both the observed HE and VHE γ -ray spectrum. The variability time scale indicates that the characteristic size of the VHE γ -ray emitting region is limited to R HE ≲ 8 rg δ GLYPH<16> ∆ t 3 days GLYPH<17> . From this, it is clear that there is a degeneracy between the observed variability timescale and δ . Hence, we will also consider cases of δ > 1 below. Similar to the EHT-oriented model, we first focus on the case of δ ∼ 1, because both a jet kinematic analysis using VLBI monitoring observations and an analysis of the brightness ratio of the approaching-jet and counter-jet suggest that the M87 jet is moving at subluminal speeds within the deprojected distance about 10 3 rg from the central SMBH (Mertens et al. 2016; Park et al. 2019, and references therein). We took as reference for the Doppler factor a value δ = 1 . 82, that is consistent with the range allowed by the assumed viewing angle ( θ view = 17 · ) and able to reproduce the SED.  \nThe 2018 SED VHE active phase further motivates us to explore the case of δ > 1. It is not straightforward to fit both the VHE γ -ray spectrum with Γ VHE ≈ 2 . 0 and the Fermi-LAT spectrum with a single emitting zone. We therefore considered two approaches: In Model A, we modified the eDF parameters such that the modelled emission accounts for the tentative hardening of the VHE spectrum in April 2018 (i.e. p 1 > p 2), and in Model B, we took into account a fast moving component. 5  \nThe best-fit for Model A is shown in Fig. 19. We note that the physical origin is not obvious and further theoretical work is needed to assess its feasibility in the case of M87.  \nIn Model B, we retain the loss-dominated approach in the single-zone for the higher energy index (i.e. p 1 < p 2). We examine the phenomenological eDF with p 1 < p 2, that is complementary to the model A, since the radiative cooling rate may dominate the acceleration rate. In this model, we consider a fast moving blob as an additional component of the SED to explain the hard H.E.S.S. γ -ray spectrum in a flaring epoch. It should be mentioned that we need this additional component not due to the softening of the high-energy eDF, but due to the appearance of the decrease of VHE γ -ray emission because of the KleinNishina e ff ect. To reproduce the H.E.S.S. γ -ray flux and avoid overshooting the observed X-ray fluxes during the flare, we set a very weak magnetic field strength of B = 4 mG and δ = 2 . 55 in the high-energy components. This model also looks consistent with the observed SED including the VHE γ -ray flare in Fig. 20. The resultant U e / U B ≃ 5 . 8 × 10 4 shown in Table 1 is very high, requiring an extremely weakly magnetised plasma. The extent to which this could be attributed to magnetic reconnection is not yet clear as current reconnection models tend to suggest that the emission region is likely to be near equipartition (e.g., Petropoulou et al. 2016). Another possibility is that the interaction between the highly magnetised jets and the weakly magnetised wind (e.g., within the jet sheath) can result in the  \nFig. 20. Model B fitted to the observed contemporaneous broadband SED of M87 for the EHT campaign in April 2018. For each considered region (EHT, high-energy, and very-high-energy flare), the synchrotron (labeled as SYN) and synchrotron self-Compton (labeled as SSC) models as well as the summation of the SYN and SSC models (solid line) are reported.  \n<!-- image -->  \ndissipation of the magnetic energy (Kusafuka et al. 2023, see also Komissarov 2012).  \nThe estimated Doppler factor of Model B's VHE-flare component is δ = 2 . 55 (the corresponding bulk Lorentz factor is ≲ 2 if the flaring component is moving along the global jet). The assumed Lorentz factor is consistent with the mildly high bulk Lorentz factor proposed by observations of M87: the gradual acceleration of the jet is suggested by high-cadence VLBI observations (Park et al. 2019), where the estimated Lorentz factor is ∼ 2 at the distance ≳ 10 3 r g, and even at the HST-1 knot, the Xray observations also suggest that the Lorentz factor of the jet is high ∼ 6 (Snios et al. 2019). The required jet power of the VHE flare component of the model B is ∼ 3 . 7 × 10 42 ergs -1 . This is compatible with the jet power exhibited in GRMHD simulations.", '4.4. X-ray broken power law oriented model': "Fig. 21 shows the Model A fit for the case of BPL X-ray radiation. The inverted component on the HE side of the X-ray spectrum can be fitted by increasing the synchrotron flux in the HE-oriented model region. This leads to slightly di ff erent best fit values, since the eDF must be fine-tuned to reduce the increase in the synchrotron self-Compton (SSC) emission component corresponding to the increased synchrotron flux. It is clear that model A explains both PL and BPL cases naturally without altering the essential parts of the model. As shown by the figures in Table 1, the di ff erence between the two cases can be attributed to the di ff erence in the physical state of the HE region. When n ' e and B ' in the HE region increase by several times, the low-energy tail part of the emission from the HE region contributes to the X-ray energy band, resulting in the realisation of BPL. Furthermore, it should be emphasised that when BPL is realised in the X-ray energy region, the advantage is that tighter constraints can be placed on the model parameters of the HE emission region through SED fitting. As can be seen from the figure, the high-energy side of the BPL spectrum is a combination of the high-energy bump of the SYN component from  \nTable 1. SED model fitting parameters for three di ff erent models used (A-PL, A-BPL and B model - for models and parameters details see Sect. 4.2 and Sect. 4.3).", 'Notes.': "- ( 1 ) For M87*, rg = 9 . 8 × 10 14 cm.\n- ( 2 )\n- Parameter pegged to the observed ring size for the EHT-oriented models and the upper limit of the allowed interval for the HE-oriented models.\n- ( 3 ) The number density of total non-thermal electrons or electron / positron pairs.\n- ( 4 ) The ratio of the nonthermal electron (or electron / positron pair) energy to the magnetic energy, that is derived for comparison between models.\n- ( 5 ) For the EHT-MWL 2017 modelling, the inferred parameters were δ = 1, R = 5 . 2 r g, n ' e = 3 . 6 × 10 5 cm -3 , B ' = 4 . 7 G, γ min = 1, γ max = 3 . 5 × 10 6 ,\n- p 1 = 2 . 2, and resulting U e / U B = 2 . 3, that are similar to those obtained for the EHT-oriented model A here.  \nthe HE region and the low-energy side of the SSC component. This SED fitting can only be accurately reproduced with a delicate balance of model parameters. In other words, it can be said that this imposes stricter constraints on the model parameters. As mentioned in Sect. 4.1, the presence of the BPL spectrum requires the bump to be located in the MeV energy band. However, we face the lack of data at MeV energies that could help provide crucial information to distinguish between the proposed models.  \nFig. 21. Modified Model A fitted describing the BPL emission seen in the Chandra -NuSTAR X-ray spectrum.  \n<!-- image -->  \nlog", '4.5. The possible origin of γ -ray emission': "One motivation to release this data set is to aid in the development of more complex models that can address the origin of the γ -ray emission, at the same time satisfying the new constraints from VLBI imaging. We discuss here potential scenarios for  \nthe generation of HE non-thermal electrons (and positrons) in a compact region with the size of the order of ∼ 10 rg or a larger region ∼ 100 rg .  \nThe potential production and acceleration of high-energy electron-positron pairs at the vacuum gap formed at the stagnation surface within an accreting BH magnetosphere, could o ff er an explanation for the observed gamma-ray flare emission (e.g., Levinson & Rieger 2011; Broderick & Tchekhovskoy 2015; Hirotani 2018; Chen & Yuan 2020; Crinquand et al. 2020; Katsoulakos & Rieger 2020; Kisaka et al. 2022, and references therein). Semi-analytical calculations based on this class of models indicate that the potential VHE emission power from this gap region is su ffi cient to explain the day-scale VHE variability from M87. While this gap-driven black hole magnetosphere model can explain TeV γ rays, it seems di ffi cult to reproduce GeV γ -ray flux due to a hard photon spectrum and a very high maximum energy of electrons (e.g., Levinson & Rieger 2011; Hirotani 2018). Therefore, other radiation mechanisms seem necessary to reproduce the observed GeV γ -ray emission.  \nMagnetic reconnection, a commonly occurring process in magnetised plasma, is capable of accelerating particles to high energies (e.g. Zenitani & Hoshino 2001; Lyubarsky & Liverts 2008; Sironi & Spitkovsky 2014; Guo et al. 2014; Werner et al. 2016). Comparison of the linearly polarised, horizon-scale emission of M87 detected by EHT with GRMHD simulations strongly suggests that M87*'s accretion flow is currently in a magnetically arrested disk (MAD) state (Event Horizon Telescope Collaboration et al. 2021a,b). The MAD state (Igumenshchev & Narayan 2002; Narayan et al. 2003, e.g.) occurs when magnetic fields carried in by the accretion flow build up on the horizon, to the point where the magnetic pressure can push back, and truncate, the accretion flow.  \nThe preference for a MAD-like state is also supported by other recent investigations on the properties of magnetic fields and velocity fields downstream of the M87 jet (Chael et al. 2019; Cruz-Osorio et al. 2022; Yuan et al. 2022; Kino et al. 2022; Fromm et al. 2022). Recent ultra-high resolution GRMHD simulations of MAD-state flows have shown that magnetic reconnec-  \ntion can occur in an equatorial current sheet just outside the event horizon as a result of magnetic flux eruptions; this process has been proposed as a possible origin of VHE γ -rays (e.g. Ripperda et al. 2020, 2022; Chashkina et al. 2021; Hakobyan et al. 2023), presenting models that are qualitatively consistent with the M87 MWL campaign observation in 2018. Furthermore, the size of the transient current layer can become of the order of 10 rg (Ripperda et al. 2022), that is qualitatively consistent with the HEoriented model of M87 2018 data. Recent studies of Gelles et al. (2022) and Jia et al. (2023) showed that during a reconnectionpowered VHE flare, the sub-millimetre / radio core flux can decline due to the temporarily ejected accretion disk, that may be similar to what we observe (see light curves in Figs. 15 and 16).  \nThe flaring VHE γ -rays could also originate via an additional fast moving component (i.e. a structured, multi-zone scenario). There are mainly two types of models for representing the fast moving component: (i) the structured jet with fast spine and slow sheath components 6 (e.g. Ghisellini et al. 2005) and (ii) a fast moving blob (or mini jet) component (e.g. Lenain, J.-P. et al. 2008; Giannios et al. 2009). The latter is considered in Model B (Sect. 4.3) to account for the VHE γ rays. There are two possible origins of this fast moving component: magnetic reconnection or shock.  \nWhether magnetic reconnection can account for the implied high U e / U B (Table 1) remains to be explored. Current (2d PIC) simulations of relativistic reconnection (e.g., Sironi et al. 2015; Petropoulou et al. 2016) rather suggest U e ∼ U B. The shock formed via the interaction between strongly magnetised jets and weakly magnetised winds is another possible origin for the blob component of the Model B. Kusafuka et al. (2023) performed one-dimensional spherically symmetric special-relativistic MHD simulations and found that ∼ 10% of the magnetic energy can be dissipated. This resulted in the acceleration of a weakly magnetised wind, that may be the emission site of the VHE γ -rays. Since the very high U e / U B ∼ 10 4 -6 is also reported in some blazars (e.g., 1ES 1101-232 and 3C 66A), with simple analysis using single-zone SSC models (Domínguez et al. 2013), it will be important to further explore to what extent a high U e / U B can be realised under di ff erent conditions.  \nAlthough we primarily focus on the leptonic model, hadronic scenarios might also play a non-negligible role in explaining the M87 SED (Reimer et al. 2004; Reynoso et al. 2011; Rieger & Aharonian 2012; Kimura & Toma 2020; Boughelilba et al. 2022; H.E.S.S. Collaboration 2023).", '4.6. Observational aid towards unveiling gamma ray origin in M87': 'While Model A agrees with magnetic reconnection on the point of the variation timescale if the reconnection takes place in the vicinity of the black hole in the MAD (e.g., see Sect. 4.1 in Ripperda et al. 2022), Model B may require shock acceleration because of a large Ue / UB . X-ray polarisation information can help discriminate these two model scenarios by constraining the nonthermal electron production mechanism. If the flare is caused by magnetic reconnection, the position angle may drastically flip with time, whereas a shock-induced flare may not produce this  \nstrong e ff ect. Observations with IXPE or other future X-ray polarimetry missions (such as eXTP; Zhang et al. 2019) could provide important information for unveiling the emission mechanisms in M87.  \nIn addition, observations carried with more sensitive VHE instruments like the upcoming Cherenkov Telescope Array (Cherenkov Telescope Array Consortium et al. 2019; Zanin et al. 2022) are of fundamental interest. Thanks to its better shorttimescale sensitivity and spectral resolution, CTA may provide new insights on future observed γ -ray flaring episodes.  \nMoreover, the sparse sampling and limited data qualities in the currently available EHT observations make it di ffi cult to draw a conclusion on the origin of the long-term PA variation at µ as scale. Further EHT images in the subsequent years will be needed to constrain the actual duration of PA variations at EHT scales and their physical origin.  \nMore extended MWL monitoring with higher observation frequency is essential for capturing future MWL flares beyond the VHE one detected from M87 in 2018. In future campaigns, we aim to extend the monitoring period, increasing the chances of identifying potential correlations between the MWL light curves and improving sensitivity to detect up to weeks-long variability. Finally, an enhanced EHT array, as well as potentially future next-generation experiments (e.g., Lico et al. 2023), will allow us to increase the observed field-of-view. This will improve our ability to probe regions near the core (e.g., the inner jet) as potential sites for γ -ray flares.', '5. Summary': "We have presented the results of the second MWL observational campaign on M87 together with M87* observations performed by the EHT in April 2018. In addition to obtaining the second most extensive quasi-simultaneous broadband spectra of M87 taken yet, we have provided details of the individual observations and light curves, with the aim of investigating the emission variability of M87 and constraining the origin of the γ -ray emission in M87. Similar to M87 MWL2017, the primary outcome of this campaign is represented by the incredible legacy data set, for which all data and some analysis scripts are available to the community via a Cyverse repository (see Appendix 5). While we defer detailed modelling to future works by the broader community, we draw preliminary conclusions using a heuristic approach to the SED modelling. As seen in 2017, the HST-1 knot component was subdominant, and the core was dominating the total flux in the radio through the X-ray bands, providing ideal conditions for combining MWL data over a large range of spatial resolutions.  \nThe main results of the 2018 EHT-MWL observational campaign on M87 can be summarised as follows:  \n- -We detected a short (approximately three days) VHE γ -ray flare, the first since 2010. The combined data from all IACTs involved in the campaign, H.E.S.S., MAGIC, and VERITAS, helped fully describe the flare duration and the shape of the light curve and indicated spectral hardening during the flare.\n- -An HE γ -ray flux increase was also detected by Fermi -LAT during the same days.\n- -A likely longer-term core flux enhancement was observed in the X-ray band by Chandra , presenting an X-ray spectrum harder than in non-flaring states, as was observed during the EHT 2017 campaign.\n- -While radio and millimetre core fluxes are compatible with (or even lower than) the emission seen in April 2017, VLBA  \n(centimetre to millimetre) radio observations present a clear change, on an annual basis, of the jet position angle within a few milli-arcsecond from the core. The EHT (millimetre) radio observations also revealed a significant variation in the position angle of the asymmetry of the ring, indicating that this might be related to changes seen at larger scales in the jet's position angle.  \n- -Although the presence of the flaring episodes allowed us to constrain the size of the VHE γ -ray emitting region in the SED modelling, the location is still uncertain. M87*'s complex, broadband SED cannot be modelled by a one-zone model. Instead, an additional second component (or even a third one as obtained for Model B) is needed to capture its emission.  \nThe results of this broadband MWL campaign of M87 o ff er a unique opportunity to investigate the origin of the γ -ray emission during a flaring episode. In particular, they highlight the importance of having coordinated MWL observations with wellsampled coverage to fully characterise the spectral variability in M87*, that likely spans several di ff erent timescales. The data sets made public as a part of this e ff ort also o ff er a rich archive for future investigations. As outlined in Sect. 1, these data add to the growing suite of observational constraints that inform GR models as well as phenomenological models, such as those presented in this work. The ∼ 17% uncertainty on testing GR is currently dominated by theoretical uncertainties, but combining the M87 SED with fits to the EHT image can reduce these errors and improve our understanding of the underlying astrophysics.  \nThe EHT-MWL observational campaigns performed with a more sensitive array in 2021 and 2022 as well as those planned for upcoming years will enable further insights for constraining the physics around M87*, such as the disk-jet connection, and the origin and mechanisms responsible for the emission of γ -ray photons. In addition, the results from the EHT observations of other sources such as Sgr A* (Event Horizon Telescope Collaboration et al. 2022a,b), 3C 279 (Kim et al. 2020), Centaurus A (Janssen et al. 2021), OJ 287, and other targets planned for the upcoming campaigns, together with their contemporaneous MWL coverage, will greatly expand the sample of objects and the impact of the MWL studies undertaken within the EHTMWL working group. The high resolution EHT millimetreVLBI images and the broadband SED information will enable deep investigation of the emission mechanisms driving AGN.", 'Data availability': 'Data products presented in this paper are available for download through the EHT Collaboration Data Webpage ( https:// eventhorizontelescope.org/for-astronomers/data ), or directly from the CyVerse repository. The repository contains the following data products:  \n- -Broadband spectrum table with frequency, flux density, its uncertainty, and instrument index (format: DAT)\n- -Sampled posterior distributions of the SED broadband spectral model (A-PL) described in Sect. 4.2 (format: DAT).\n- -Sampled posterior distributions of the SED broadband spectral model (A-BPL) described in Sect. 4.3 (format: DAT).\n- -Sampled posterior distributions of the SED broadband spectral model (B) described in Sect. 4.3 (format: DAT).  \nAcknowledgements. The full acknowledgements are available in Appendix A.', 'References': 'Abdo, A. A., Ackermann, M., Ajello, M., et al. 2009, Phys. Rev. D, 80, 122004 Abdollahi, S., Acero, F., Ackermann, M., et al. 2020, ApJS, 247, 33  \n- Abramowski, A., Acero, F., Aharonian, F., et al. 2012, ApJ, 746, 151\n- Acciari, V. A., Aliu, E., Arlen, T., et al. 2010, ApJ, 716, 819\n- Acciari, V. A., Aliu, E., Arlen, T., et al. 2009, Science, 325, 444\n- Acciari, V. 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Box 2018120, New Haven, CT 06520, USA\n- 4 Centre for Space Research, North-West University, Potchefstroom 2520, South Africa\n- 5 Physical Research Laboratory, Ahmedabad, Gujarat, 380009, India\n- 6 Korea Astronomy and Space Science Institute, Daedeok-daero 776, Yuseong-gu, Daejeon 34055, Republic of Korea\n- 7 University of Science and Technology, Gajeong-ro 217, Yuseonggu, Daejeon 34113, Republic of Korea\n- 8 Research Center for Astronomical Computing, Zhejiang Lab, Hangzhou 311100, People\'s Republic of China\n- 9 INAF Istituto di Radioastronomia, Via P. Gobetti, 101, I-40129 Bologna, Italy\n- 10 525 Davey Laboratory, Department of Astronomy and Astrophysics, Pennsylvania State University, University Park, PA 16802, USA\n- 11 Department of Physics, McGill University, 3600 University Street, Montréal, QC H3A 2T8, Canada\n- 12 Trottier Space Institute at McGill, 3550 University Street, Montréal, QC H3A 2A7, Canada\n- 13 Department of Astrophysics, Institute for Mathematics, Astrophysics and Particle Physics (IMAPP), Radboud University, P.O. Box 9010, 6500 GL Nijmegen, The Netherlands\n- 14 Leiden Observatory-Allegro, Leiden University, P.O. Box 9513, 2300 RA Leiden, The Netherlands\n- 15 Center for Astrophysics | Harvard & Smithsonian, Cambridge, MA 02138, USA\n- 16 Mizusawa VLBI Observatory, National Astronomical Observatory of Japan, 2-12 Hoshigaoka, Mizusawa, Oshu, Iwate 023-0861, Japan\n- 17 Astronomical Science Program, The Graduate University for Advanced Studies (SOKENDAI), 2-21-1 Osawa, Mitaka, Tokyo 1818588, Japan\n- 18 Graduate School of Science, Nagoya City University, Yamanohata 1, Mizuho-cho, Mizuho-ku, Nagoya, 467-8501, Aichi, Japan\n- 19 McGill Space Institute, McGill University, 3550 University Street, Montréal, QC H3A 2A7, Canada\n- 20 Institute for Astrophysical Research, Boston University, 725 Commonwealth Ave., Boston 02215, MA, USA\n- 21 Department of Astronomy and Astrophysics, Pennsylvania State University, University Park, PA 16802, USA\n- 22 Institute for Cosmic Ray Research, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8582, Japan\n- 23 Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, D53121 Bonn, Germany\n- 24 National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan\n- 25 Kogakuin University of Technology & Engineering, Academic Support Center, 2665-1 Nakano, Hachioji, Tokyo 192-0015, Japan\n- 26 Shanghai Astronomical Observatory, Chinese Academy of Sciences, 80 Nandan Road, Shanghai 200030, People\'s Republic of China\n- 27 Key Laboratory of Radio Astronomy, Chinese Academy of Sciences, Nanjing 210008, People\'s Republic of China\n- 28 API-Anton Pannekoek Institute for Astronomy, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands\n- 29 GRAPPA-Gravitation and AstroParticle Physics Amsterdam, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands\n- 30 Department of Physics and Astronomy, Northwestern University, 2145 Sheridan Rd, Evanston, IL 60208, USA\n- 31 Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), Northwestern University, 1800 Sherman Ave, Evanston, IL 60201, USA  \n- 32 Department of Physics, Villanova University, 800 E. Lancaster Avenue, Villanova, PA 19085, USA\n- 33 Physics Department, Washington University CB 1105, St Louis, MO63130, USA\n- 34 Dipartimento di Fisica, Università di Trieste, I-34127 Trieste, Italy\n- 35 Istituto Nazionale di Fisica Nucleare, Sezione di Trieste, I-34127 Trieste, Italy\n- 36 Finnish Centre for Astronomy with ESO, FI-20014 University of Turku, Finland\n- 37 Aalto University Metsähovi Radio Observatory, Metsähovintie 114, FI-02540 Kylmälä, Finland\n- 38 Center for Computational Astrophysics, Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA\n- 39 Department of Astrophysical Sciences, Peyton Hall, Princeton University, Princeton, NJ 08544, USA\n- 40 Hiroshima Astrophysical Science Center, Hiroshima University, 13-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8526, Japan\n- 41 Massachusetts Institute of Technology Haystack Observatory, 99 Millstone Road, Westford, MA 01886, USA\n- 42 Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA\n- 43 Instituto de Astrofísica de Andalucía-CSIC, Glorieta de la Astronomía s / n, E-18008 Granada, Spain\n- 44 Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, D53121 Bonn, Germany\n- 45 Department of Physics & Astronomy, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA\n- 46 Institute of Astronomy and Astrophysics, Academia Sinica, 11F of Astronomy-Mathematics Building, AS / NTU No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan, R.O.C.\n- 47 Departament d\'Astronomia i Astrofísica, Universitat de València, C. Dr. Moliner 50, E-46100 Burjassot, València, Spain\n- 48 Observatori Astronòmic, Universitat de València, C. Catedrático José Beltrán 2, E-46980 Paterna, València, Spain\n- 49 Department of Space, Earth and Environment, Chalmers University of Technology, Onsala Space Observatory, SE-43992 Onsala, Sweden\n- 50 Steward Observatory and Department of Astronomy, University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721, USA\n- 51 Astronomy Department, Universidad de Concepción, Casilla 160C, Concepción, Chile\n- 52 Department of Physics, University of Illinois, 1110 West Green Street, Urbana, IL 61801, USA\n- 53 Fermi National Accelerator Laboratory, MS209, P.O. Box 500, Batavia, IL 60510, USA\n- 54 Department of Astronomy and Astrophysics, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, USA\n- 55 East Asian Observatory, 660 N. A\'ohoku Place, Hilo, HI 96720, USA\n- 56 James Clerk Maxwell Telescope (JCMT), 660 N. A\'ohoku Place, Hilo, HI 96720, USA\n- 57 California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125, USA\n- 58 Institute of Astronomy and Astrophysics, Academia Sinica, 645 N. A\'ohoku Place, Hilo, HI 96720, USA\n- 59 Department of Physics and Astronomy, University of Hawaii at Manoa, 2505 Correa Road, Honolulu, HI 96822, USA\n- 60 Department of Physics, McGill University, 3600 rue University, Montréal, QC H3A 2T8, Canada\n- 61 Trottier Space Institute at McGill, 3550 rue University, Montréal, QC H3A 2A7, Canada\n- 62 Institut de Radioastronomie Millimétrique (IRAM), 300 rue de la Piscine, F-38406 Saint Martin d\'Hères, France\n- 63 Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo, ON N2L 2Y5, Canada\n- 64 Department of Physics and Astronomy, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada\n- 65 Waterloo Centre for Astrophysics, University of Waterloo, Waterloo, ON N2L 3G1, Canada  \n- 66 Department of Astronomy, University of Massachusetts, Amherst, MA01003, USA\n- 67 Kavli Institute for Cosmological Physics, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, USA\n- 68 Department of Physics, University of Chicago, 5720 South Ellis Avenue, Chicago, IL 60637, USA\n- 69 Enrico Fermi Institute, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, USA\n- 70 Princeton Gravity Initiative, Jadwin Hall, Princeton University, Princeton, NJ 08544, USA\n- 71 Data Science Institute, University of Arizona, 1230 N. Cherry Ave., Tucson, AZ 85721, USA\n- 72 Program in Applied Mathematics, University of Arizona, 617 N. Santa Rita, Tucson, AZ 85721, USA\n- 73 Cornell Center for Astrophysics and Planetary Science, Cornell University, Ithaca, NY 14853, USA\n- 74 Department of Astronomy, Yonsei University, Yonsei-ro 50, Seodaemun-gu, 03722 Seoul, Republic of Korea\n- 75 Physics Department, Fairfield University, 1073 North Benson Road, Fairfield, CT 06824, USA\n- 76 Department of Astronomy, University of Illinois at UrbanaChampaign, 1002 West Green Street, Urbana, IL 61801, USA\n- 77\n- Instituto de Astronomía, Universidad Nacional Autónoma de México (UNAM), Apdo Postal 70-264, Ciudad de México, México\n- 78 Institut für Theoretische Physik, Goethe-Universität Frankfurt, Max-von-Laue-Straße 1, D-60438 Frankfurt am Main, Germany\n- 79 Research Center for Intelligent Computing Platforms, Zhejiang Laboratory, Hangzhou 311100, China\n- 80 Tsung-Dao Lee Institute, Shanghai Jiao Tong University, Shengrong Road 520, Shanghai, 201210, People\'s Republic of China\n- 81 Department of Astronomy and Columbia Astrophysics Laboratory, Columbia University, 500 W. 120th Street, New York, NY 10027, USA\n- 82 Center for Computational Astrophysics, Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA\n- 83 Dipartimento di Fisica \'E. Pancini\', Università di Napoli \'Federico II\', Compl. Univ. di Monte S. Angelo, Edificio G, Via Cinthia, I80126, Napoli, Italy\n- 84 INFN Sez. di Napoli, Compl. Univ. di Monte S. Angelo, Edificio G, Via Cinthia, I-80126, Napoli, Italy\n- 85 Wits Centre for Astrophysics, University of the Witwatersrand, 1 Jan Smuts Avenue, Braamfontein, Johannesburg 2050, South Africa\n- 86 Department of Physics, University of Pretoria, Hatfield, Pretoria 0028, South Africa\n- 87 Centre for Radio Astronomy Techniques and Technologies, Department of Physics and Electronics, Rhodes University, Makhanda 6140, South Africa\n- 88 ASTRON, Oude Hoogeveensedijk 4, 7991 PD Dwingeloo, The Netherlands\n- 89 LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université de Paris, 5 place Jules Janssen, F-92195 Meudon, France\n- 90 JILA and Department of Astrophysical and Planetary Sciences, University of Colorado, Boulder, CO 80309, USA\n- 91 National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing 100101, PR China\n- 92 Las Cumbres Observatory, 6740 Cortona Drive, Suite 102, Goleta, CA 93117-5575, USA\n- 93 Department of Physics, University of California, Santa Barbara, CA 93106-9530, USA\n- 94 National Radio Astronomy Observatory, 520 Edgemont Road, Charlottesville, VA 22903, USA\n- 95 Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32-D476, 77 Massachusetts Ave., Cambridge, MA 02142, USA\n- 96 Google Research, 355 Main St., Cambridge, MA 02142, USA\n- 97 Institut für Theoretische Physik und Astrophysik, Universität Würzburg, Emil-Fischer-Str. 31, D-97074 Würzburg, Germany  \n- 98 Department of History of Science, Harvard University, Cambridge, MA02138, USA\n- 99 Department of Physics, Harvard University, Cambridge, MA 02138, USA\n- 100 NCSA, University of Illinois, 1205 W. Clark St., Urbana, IL 61801, USA\n- 101 Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, R. do Matão, 1226, São Paulo, SP 05508090, Brazil\n- 102 CP3-Origins, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark\n- 103 Instituto Nacional de Astrofísica, Óptica y Electrónica. Apartado Postal 51 y 216, 72000. Puebla Pue., México\n- 104 Consejo Nacional de Humanidades, Ciencia y Tecnología, Av. Insurgentes Sur 1582, 03940, Ciudad de México, México\n- 105 Key Laboratory for Research in Galaxies and Cosmology, Chinese Academy of Sciences, Shanghai 200030, People\'s Republic of China\n- 106 NOVA Sub-mm Instrumentation Group, Kapteyn Astronomical Institute, University of Groningen, Landleven 12, 9747 AD Groningen, The Netherlands\n- 107 Department of Astronomy, School of Physics, Peking University, Beijing 100871, People\'s Republic of China\n- 108 Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing 100871, People\'s Republic of China\n- 109 Department of Astronomy, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan\n- 110 The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan\n- 111 Department of Statistical Science, The Graduate University for Advanced Studies (SOKENDAI), 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan\n- 112 Kavli Institute for the Physics and Mathematics of the Universe, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 277-8583, Japan\n- 113 Leiden Observatory, Leiden University, Postbus 2300, 9513 RA Leiden, The Netherlands\n- 114 ASTRAVEO LLC, PO Box 1668, Gloucester, MA 01931\n- 115 Applied Materials Inc., 35 Dory Road, Gloucester, MA 01930\n- 116 Joint Institute for VLBI ERIC (JIVE), Oude Hoogeveensedijk 4, 7991 PD Dwingeloo, The Netherlands\n- 117 Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea\n- 118 Graduate School of Science and Technology, Niigata University, 8050 Ikarashi 2-no-cho, Nishi-ku, Niigata 950-2181, Japan\n- 119 Physics Department, National Sun Yat-Sen University, No. 70, Lien-Hai Road, Kaosiung City 80424, Taiwan, R.O.C.\n- 120 School of Astronomy and Space Science, Nanjing University, Nanjing 210023, People\'s Republic of China\n- 121 Key Laboratory of Modern Astronomy and Astrophysics, Nanjing University, Nanjing 210023, People\'s Republic of China\n- 122 INAF-Istituto di Radioastronomia, Via P. Gobetti 101, I-40129 Bologna, Italy\n- 123 INAF-Istituto di Radioastronomia & Italian ALMA Regional Centre, Via P. Gobetti 101, I-40129 Bologna, Italy\n- 124 Department of Physics, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan, R.O.C\n- 125 Instituto de Radioastronomía y Astrofísica, Universidad Nacional Autónoma de México, Morelia 58089, México\n- 126 Yunnan Observatories, Chinese Academy of Sciences, 650011 Kunming, Yunnan Province, People\'s Republic of China\n- 127 Center for Astronomical Mega-Science, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing, 100012, People\'s Republic of China\n- 128 Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, 650011 Kunming, People\'s Republic of China\n- 129 Department of Astrophysical Sciences, Peyton Hall, Princeton University, Princeton, NJ 08544, USA\n- 130 School of Physics and Astronomy, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, People\'s Republic of China\n- 131 Institut de Radioastronomie Millimétrique (IRAM), Avenida Divina Pastora 7, Local 20, E-18012, Granada, Spain\n- 132 National Institute of Technology, Hachinohe College, 16-1 Uwanotai, Tamonoki, Hachinohe City, Aomori 039-1192, Japan\n- 133 Astronomy Department, Universidad de Concepción, Casilla 160C, Concepción, Chile Research Center for Astronomy, Academy of Athens, Soranou Efessiou 4, 115 27 Athens, Greece\n- 134 School of Physics, Georgia Institute of Technology, 837 State St NW, Atlanta, GA 30332, USA\n- 135 Department of Astronomy and Space Science, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggido 17104, Republic of Korea\n- 136 Canadian Institute for Theoretical Astrophysics, University of Toronto, 60 St. George Street, Toronto, ON M5S 3H8, Canada\n- 137 Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada\n- 138 Canadian Institute for Advanced Research, 180 Dundas St West, Toronto, ON M5G 1Z8, Canada\n- 139 Department of Physics, National Taiwan Normal University, No. 88, Sec. 4, Tingzhou Rd., Taipei 116, Taiwan, R.O.C.\n- 140 Center of Astronomy and Gravitation, National Taiwan Normal University, No. 88, Sec. 4, Tingzhou Road, Taipei 116, Taiwan, R.O.C.\n- 141 Aalto University Metsähovi Radio Observatory, Metsähovintie 114, FI-02540 Kylmälä, Finland\n- 142 Gemini Observatory / NSF\'s NOIRLab, 670 N. Aòh¯ok¯u Place, Hilo, HI 96720, USA\n- 143 Frankfurt Institute for Advanced Studies, Ruth-Moufang-Strasse 1, D-60438 Frankfurt, Germany\n- 144 School of Mathematics, Trinity College, Dublin 2, Ireland\n- 145 Department of Physics, University of Toronto, 60 St. George Street, Toronto, ON M5S 1A7, Canada\n- 146 Aalto University Department of Electronics and Nanoengineering, PL 15500, FI-00076 Aalto, Finland\n- 147 Jeremiah Horrocks Institute, University of Central Lancashire, Preston PR1 2HE, UK\n- 148 National Biomedical Imaging Center, Peking University, Beijing 100871, People\'s Republic of China\n- 149 College of Future Technology, Peking University, Beijing 100871, People\'s Republic of China\n- 150 Tokyo Electron Technology Solutions Limited, 52 Matsunagane, Iwayado, Esashi, Oshu, Iwate 023-1101, Japan\n- 151 Department of Physics and Astronomy, University of Lethbridge, Lethbridge, Alberta T1K 3M4, Canada\n- 152 Netherlands Organisation for Scientific Research (NWO), Postbus 93138, 2509 AC Den Haag, The Netherlands\n- 153 Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai 980-8578, Japan\n- 154 Astronomical Institute, Tohoku University, Sendai 980-8578, Japan\n- 155 Department of Physics and Astronomy, Seoul National University, Gwanak-gu, Seoul 08826, Republic of Korea\n- 156 University of New Mexico, Department of Physics and Astronomy, Albuquerque, NM 87131, USA\n- 157 Physics Department, Brandeis University, 415 South Street, Waltham, MA 02453, USA\n- 158 Tuorla Observatory, Department of Physics and Astronomy, University of Turku, Finland\n- 159 Radboud Excellence Fellow of Radboud University, Nijmegen, The Netherlands\n- 160 School of Natural Sciences, Institute for Advanced Study, 1 Einstein Drive, Princeton, NJ 08540, USA\n- 161 School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, People\'s Republic of China\n- 162 Mullard Space Science Laboratory, University College London, Holmbury St. Mary, Dorking, Surrey, RH5 6NT, UK  \n- 163 Center for Astronomy and Astrophysics and Department of Physics, Fudan University, Shanghai 200438, People\'s Republic of China\n- 164 Astronomy Department, University of Science and Technology of China, Hefei 230026, People\'s Republic of China\n- 165 Department of Physics and Astronomy, Michigan State University, 567 Wilson Rd, East Lansing, MI 48824, USA\n- 166 Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy\n- 167 California State University, Los Angeles, Department of Physics and Astronomy, Los Angeles, CA 90032, USA\n- 168 Dipartimento di Fisica \'M. Merlin" dell\'Università e del Politecnico di Bari, via Amendola 173, I-70126 Bari, Italy\n- 169 Istituto Nazionale di Fisica Nucleare, Sezione di Bari, I-70126 Bari, Italy\n- 170 W. W. Hansen Experimental Physics Laboratory, Kavli Institute for Particle Astrophysics and Cosmology, Department of Physics and SLAC National Accelerator Laboratory, Stanford University, Stanford, CA 94305, USA\n- 171 Istituto Nazionale di Fisica Nucleare, Sezione di Torino, I-10125 Torino, Italy\n- 172 Dipartimento di Fisica, Università degli Studi di Torino, I-10125 Torino, Italy\n- 173 Laboratoire Leprince-Ringuet, CNRS / IN2P3, \'Ecole polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France\n- 174 INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano, via E. Bassini 15, I-20133 Milano, Italy  \n175  \nItalian Space Agency, Via del Politecnico snc, 00133 Roma, Italy  \n- 176 Space Science Division, Naval Research Laboratory, Washington, DC 20375-5352, USA\n- 177 Istituto Nazionale di Fisica Nucleare, Sezione di Roma \'Tor Vergata", I-00133 Roma, Italy\n- 178 Space Science Data Center - Agenzia Spaziale Italiana, Via del Politecnico, snc, I-00133, Roma, Italy\n- 179 Dipartimento di Fisica, Università degli Studi di Perugia, I-06123 Perugia, Italy\n- 180 Istituto Nazionale di Fisica Nucleare, Sezione di Perugia, I-06123 Perugia, Italy\n- 181 Grupo de Altas Energ\'ias, Universidad Complutense de Madrid, E28040 Madrid, Spain\n- 182 Ruhr University Bochum, Faculty of Physics and Astronomy, Astronomical Institute (AIRUB), 44780 Bochum, Germany\n- 183 Department of Physical Sciences, Hiroshima University, HigashiHiroshima, Hiroshima 739-8526, Japan\n- 184 Dipartimento di Fisica, Università di Roma \'Tor Vergata", I-00133 Roma, Italy\n- 185 Universit\'e Paris Cité, Université Paris-Saclay, CEA, CNRS, AIM, F-91191 Gif-sur-Yvette, France\n- 186 NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA\n- 187 Department of Physics, KTH Royal Institute of Technology, AlbaNova, SE-106 91 Stockholm, Sweden\n- 188 The Oskar Klein Centre for Cosmoparticle Physics, AlbaNova, SE106 91 Stockholm, Sweden\n- 189 NASA Marshall Space Flight Center, Huntsville, AL 35812, USA\n- 190 The Aerospace Corporation, 14745 Lee Rd, Chantilly, VA 20151, USA\n- 191 Department of Physics and Center for Space Sciences and Technology, University of Maryland Baltimore County, Baltimore, MD 21250, USA\n- 192 Hiroshima Astrophysical Science Center, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8526, Japan\n- 193 Vatican Observatory, V-00120 Castel Gandolfo, Vatican City State\n- 194 Department of physics and Astronomy, Louisiana State University, Baton Rouge, LA 70803, USA\n- 195 INAF-Astronomical Observatory of Padova, Vicolo dell\'Osservatorio 5, I-35122 Padova, Italy\n- 196 Institut für Astround Teilchenphysik, Leopold-FranzensUniversität Innsbruck, A-6020 Innsbruck, Austria\n- 197 Instituto de Física Teórica UAM / CSIC, Universidad Autónoma de Madrid, E-28049 Madrid, Spain  \n- 198 Departamento de Física Teórica, Universidad Autónoma de Madrid, 28049 Madrid, Spain\n- 199 Santa Cruz Institute for Particle Physics Department of Physics and Department of Astronomy and Astrophysics, University of California at Santa Cruz, Santa Cruz, CA 95064, USA\n- 200 NYCB Real-Time Computing Inc., Lattingtown, NY 11560-1025, USA\n- 201 Purdue University Northwest, Hammond, IN 46323, USA\n- 202 Nagoya University, Institute for Space-Earth Environmental Research, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan\n- 203 Kobayashi-Maskawa Institute for the Origin of Particles and the Universe, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan\n- 204 Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Magrans s / n, E-08193 Barcelona, Spain; and Institut d\'Estudis Espacials de Catalunya (IEEC), E-08034 Barcelona, Spain\n- 205 Instituci\'o Catalana de Recerca i Estudis Avanccats (ICREA), E08010 Barcelona, Spain\n- 206 Center for Astrophysics and Cosmology, University of Nova Gorica, Nova Gorica, Slovenia\n- 207 Dublin Institute for Advanced Studies, 31 Fitzwilliam Place, Dublin 2, Ireland\n- 208 Max-Planck-Institut für Kernphysik, Saupfercheckweg 1, 69117 Heidelberg, Germany\n- 209 Yerevan State University, 1 Alek Manukyan St, Yerevan 0025, Armenia\n- 210 Yerevan State University, 1 Alek Manukyan St., Yerevan 0025, Armenia\n- 211 Landessternwarte, Universität Heidelberg, Königstuhl, D 69117 Heidelberg, Germany\n- 212 Kapteyn Astronomical Institute, University of Groningen, Landleven 12, 9747 AD Groningen, The Netherlands\n- 213 Laboratoire Leprince-Ringuet, École Polytechnique, CNRS, Institut Polytechnique de Paris, F-91128 Palaiseau, France\n- 214 Deutsches Elektronen-Synchrotron DESY, Platanenallee 6, 15738 Zeuthen, Germany\n- 215 Institut für Physik und Astronomie, Universität Potsdam, KarlLiebknecht-Strasse 24 / 25, D 14476 Potsdam, Germany\n- 216 Université de Paris, CNRS, Astroparticule et Cosmologie, F-75013 Paris, France\n- 217 Department of Physics and Electrical Engineering, Linnaeus University, 351 95 Växjö, Sweden\n- 218 Institut für Physik, Humboldt-Universität zu Berlin, Newtonstr. 15, D 12489 Berlin, Germany\n- 219 Laboratoire Univers et Théories, Observatoire de Paris, Université PSL, CNRS, Université Paris Cité, 5 Pl. Jules Janssen, 92190 Meudon, France\n- 220 Sorbonne Université, CNRS / IN2P3, Laboratoire de Physique Nucléaire et de Hautes Energies, LPNHE, 4 place Jussieu, 75005 Paris, France\n- 221 IRFU, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France\n- 222 University of Oxford, Department of Physics, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK\n- 223 Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen Centre for Astroparticle Physics, Nikolaus-Fiebiger-Str. 2, 91058 Erlangen, Germany\n- 224 Astronomical Observatory, The University of Warsaw, Al. Ujazdowskie 4, 00-478 Warsaw, Poland\n- 225 Instytut Fizyki Ja¸drowej PAN, ul. Radzikowskiego 152, 31-342 Kraków, Poland\n- 226 Universität Hamburg, Institut für Experimentalphysik, Luruper Chaussee 149, D 22761 Hamburg, Germany\n- 227 School of Physics, University of the Witwatersrand, 1 Jan Smuts Avenue, Braamfontein, Johannesburg, 2050 South Africa\n- 228 Laboratoire Univers et Particules de Montpellier, Université Montpellier, CNRS / IN2P3, CC 72, Place Eugène Bataillon, F-34095 Montpellier Cedex 5, France\n- 229 School of Physical Sciences, University of Adelaide, Adelaide 5005, Australia  \n- 230 Aix Marseille Université, CNRS / IN2P3, CPPM, Marseille, France\n- 231 Institut für Astronomie und Astrophysik, Universität Tübingen, Sand 1, D 72076 Tübingen, Germany\n- 232 Universität Innsbruck, Institut für Astro- und Teilchenphysik, Technikerstraße 25, 6020 Innsbruck, Austria\n- 233 Obserwatorium Astronomiczne, Uniwersytet Jagiello\'nski, ul. Orla 171, 30-244 Kraków, Poland\n- 234 University of Namibia, Department of Physics, Private Bag 13301, Windhoek 10005, Namibia\n- 235 Institute of Astronomy, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Torun, Poland\n- 236 Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, ul. Bartycka 18, 00-716 Warsaw, Poland\n- 237 Université Bordeaux, CNRS, LP2I Bordeaux, UMR 5797, F-33170 Gradignan, France\n- 238 University of Southern Denmark, DK-5230 Odense, Denmark\n- 239 Department of Physics and Astronomy, The University of Leicester, University Road, Leicester, LE1 7RH, United Kingdom\n- 240 Department of Physics, Konan University, 8-9-1 Okamoto, Higashinada, Kobe, Hyogo 658-8501, Japan\n- 241 Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, 5-1-5 Kashiwa-no-Ha, Kashiwa, Chiba, 277-8583, Japan\n- 242 Japanese MAGIC Group: Institute for Cosmic Ray Research\n- (ICRR), The University of Tokyo, Kashiwa, 277-8582 Chiba, Japan\n- 243 ETH Zürich, CH-8093 Zürich, Switzerland\n- 244 Università di Siena and INFN Pisa, I-53100 Siena, Italy\n- 245 Institut de Física d\'Altes Energies (IFAE), The Barcelona Institute of Science and Technology (BIST), E-08193 Bellaterra (Barcelona), Spain\n- 246 Universitat de Barcelona, ICCUB, IEEC-UB, E-08028 Barcelona, Spain\n- 247 Instituto de Astrofísica de Andalucía-CSIC, Glorieta de la Astronomía s / n, 18008, Granada, Spain\n- 248 National Institute for Astrophysics (INAF), I-00136 Rome, Italy\n- 249 Università di Udine and INFN Trieste, I-33100 Udine, Italy\n- 250 Max-Planck-Institut für Physik, D-85748 Garching, Germany\n- 251 Università di Padova and INFN, I-35131 Padova, Italy\n- 252 Croatian MAGIC Group: University of Zagreb, Faculty of Electrical Engineering and Computing (FER), 10000 Zagreb, Croatia\n- 253 Centro Brasileiro de Pesquisas Físicas (CBPF), 22290-180 URCA, Rio de Janeiro (RJ), Brazil\n- 254 IPARCOS Institute and EMFTEL Department, Universidad Complutense de Madrid, E-28040 Madrid, Spain\n- 255 Instituto de Astrofísica de Canarias and Dpto. de Astrofísica, Universidad de La Laguna, E-38200, La Laguna, Tenerife, Spain\n- 256 University of Lodz, Faculty of Physics and Applied Informatics, Department of Astrophysics, 90-236 Lodz, Poland\n- 257 Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, E-28040 Madrid, Spain\n- 258 Departament de Física, and CERES-IEEC, Universitat Autònoma de Barcelona, E-08193 Bellaterra, Spain\n- 259 Università di Pisa and INFN Pisa, I-56126 Pisa, Italy\n- 260 INFN MAGIC Group: INFN Sezione di Bari and Dipartimento Interateneo di Fisica dell\'Università e del Politecnico di Bari, I-70125 Bari, Italy\n- 261 Department for Physics and Technology, University of Bergen, Norway\n- 262 INFN MAGIC Group: INFN Sezione di Torino and Università degli Studi di Torino, I-10125 Torino, Italy\n- 263 Croatian MAGIC Group: University of Rijeka, Faculty of Physics, 51000 Rijeka, Croatia\n- 264 Universität Würzburg, D-97074 Würzburg, Germany\n- 265 Technische Universität Dortmund, D-44221 Dortmund, Germany\n- 266 Japanese MAGIC Group: Physics Program, Graduate School of Advanced Science and Engineering, Hiroshima University, 7398526 Hiroshima, Japan\n- 267 Armenian MAGIC Group: ICRANet-Armenia, 0019 Yerevan, Armenia\n- 268 Croatian MAGIC Group: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB), 21000 Split, Croatia\n- 269 Croatian MAGIC Group: Josip Juraj Strossmayer University of Osijek, Department of Physics, 31000 Osijek, Croatia\n- 270 Finnish MAGIC Group: Finnish Centre for Astronomy with ESO, Department of Physics and Astronomy, University of Turku, FI20014 Turku, Finland\n- 271 Japanese MAGIC Group: Department of Physics, Tokai University, Hiratsuka, 259-1292 Kanagawa, Japan\n- 272 University of Geneva, Chemin d\'Ecogia 16, CH-1290 Versoix, Switzerland\n- 273 Saha Institute of Nuclear Physics, A CI of Homi Bhabha National Institute, Kolkata 700064, West Bengal, India\n- 274 Inst. for Nucl. Research and Nucl. Energy, Bulgarian Academy of Sciences, BG-1784 Sofia, Bulgaria\n- 275 Japanese MAGIC Group: Department of Physics, Yamagata University, Yamagata 990-8560, Japan\n- 276 Finnish MAGIC Group: Space Physics and Astronomy Research Unit, University of Oulu, FI-90014 Oulu, Finland\n- 277 Japanese MAGIC Group: Chiba University, ICEHAP, 263-8522 Chiba, Japan\n- 278 Japanese MAGIC Group: Institute for Space-Earth Environmental Research and Kobayashi-Maskawa Institute for the Origin of Particles and the Universe, Nagoya University, 464-6801 Nagoya, Japan  \n279  \nJapanese MAGIC Group: Department of Physics, Kyoto Univer-  \nsity, 606-8502 Kyoto, Japan  \n- 280 INFN MAGIC Group: INFN Roma Tor Vergata, I-00133 Roma, Italy\n- 281 Japanese MAGIC Group: Department of Physics, Konan University, Kobe, Hyogo 658-8501, Japan\n- 282 also at International Center for Relativistic Astrophysics (ICRA), Rome, Italy\n- 283 also at Port d\'Informació Científica (PIC), E-08193 Bellaterra (Barcelona), Spain\n- 284 also at Department of Physics, University of Oslo, Norway\n- 285 Max-Planck-Institut für Physik, D-85748 Garching, Germany\n- 286 also at INAF Padova, I-35122 Padova, Italy\n- 287 Japanese MAGIC Group: Institute for Cosmic Ray Research (ICRR), The University of Tokyo, Kashiwa, 277-8582 Chiba, Japan\n- 288 CP3-Origins, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark\n- 289 Physics Department, Columbia University, New York, NY 10027, USA\n- 290 Department of Physics and Astronomy and the Bartol Research Institute, University of Delaware, Newark, DE 19716, USA\n- 291 Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112, USA\n- 292 Physics Department, California Polytechnic State University, San Luis Obispo, CA 94307, USA\n- 293 Department of Physics, Washington University, St. Louis, MO 63130, USA\n- 294 Department of Physics, California State University - East Bay, Hayward, CA 94542, USA\n- 295 School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA\n- 296 Santa Cruz Institute for Particle Physics and Department of Physics, University of California, Santa Cruz, CA 95064, USA\n- 297 Department of Physics and Astronomy, University of Iowa, Van Allen Hall, Iowa City, IA 52242, USA\n- 298 Department of Physics and Astronomy, Iowa State University, Ames, IA 50011, USA\n- 299 Department of Physics and Astronomy, DePauw University, Greencastle, IN 46135-0037, USA\n- 300 School of Physics, University College Dublin, Belfield, Dublin 4, Ireland\n- 301 Department of Physics and Astronomy, Dartmouth College, 6127 Wilder Laboratory, Hanover, NH 03755, USA  \n- 302 Department of Physics, University of Maryland, College Park, MD, USA\n- 303 School of Natural Sciences, University of Galway, University Road, Galway, H91 TK33, Ireland\n- 304 Physics Department, McGill University, Montreal, QC H3A 2T8, Canada\n- 305 Department of Physics and Astronomy, Purdue University, West Lafayette, IN 47907, USA\n- 306 Department of Physics and Astronomy, University of California, Los Angeles, CA 90095, USA\n- 307 Arthur B. McDonald Canadian Astroparticle Physics Research Institute, 64 Bader Lane, Queen\'s University, Kingston, ON Canada, K7L 3N6\n- 308 Institute of Physics and Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany\n- 309 Fakultät für Physik & Astronomie, Ruhr-Universität Bochum, D44780 Bochum, Germany\n- 310 School of Physics, University College Dublin, Belfield, Dublin 4, Ireland\n- 311 Department of Physical Sciences, Munster Technological University, Bishopstown, Cork, T12 P928, Ireland\n- 312 Department of Physics, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA\n- 313 Department of Physics, University of Maryland, Baltimore County, Baltimore MD 21250, USA\n- 314 Department of Physics and Astronomy, Barnard College, Columbia University, NY 10027, USA\n- 315 Department of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487, USA\n- 316 NASA GSFC, Greenbelt, MD 20771, USA\n- 317 Key Laboratory of Radio Astronomy and Technology, Chinese Academy of Sciences, A20 Datun Road, Chaoyang District, Beijing 100101, PR China\n- 318 Xinjiang Astronomical Observatory, Chinese Academy of Sciences, 150 Science 1-Street, Urumqi 830011, People\'s Republic of China\n- 319 Graduate School of Sciences and Technology for Innovation, Yamaguchi University, J-753-0841 Yamaguchi, Japan\n- 320 The Research Institute for Time Studies, J-753-0841 Yamaguchi, Japan University\n- 321 Korea Astronomy and Space Science Institute, Daedeok-daero 776, Yuseong-gu, Daejeon 34055, Republic of Korea\n- 322 National Astronomical Research Institute of Thailand (NARIT), 260 Moo 4, T. Donkaew, A. Maerim, 50180 Chiangmai, Thailand\n- 323 Graduate School of Science, Osaka Metropolitan University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan\n- 324 Center for Astronomy, Ibaraki University, 2-1-1 Bunkyo, Mito, Ibaraki 310-8512, Japan', 'Appendix A: Acknowledgements': 'Individual author contribution: G. Principe: project leadership, Fermi-LAT data analysis, MWL variability and SED study, results interpretation, paper writing; J. C. Algaba: MWL images study; C. Arcaro: H.E.S.S. analysis; M. Balokovic: X-ray data analysis and interpretation ; V. Barbosa Martins: H.E.S.S. analysis, results interpretation, and paper writing; S. Chandra: AstroSAT data analysis; Y.-Z. Cui: VERA, EAVN / KaVA data analysis, and radio properties study; F. D\'Ammando: SwiftUVOT; A.D. Falcone: Swift-XRT analysis; N. M. Ford: X-ray results interpretation; D. Glawion: H.E.S.S. analysis and paper writing; M. Giroletti: Fermi-LAT data crosscheck and paper writing; C. Goddi: ALMA data analysis; M. A. Gurwell: SMA data analysis; K. Hada: VERA, EAVN / KaVA data analysis, radio results interpretation, and paper writing; D. Haggard: Xray analysis, MWL results interpretation and paper writing; A. Hahn: MAGIC data analysis, MWL variability study, paper writing, and IACT cross-normalisation analysis; W. Jin: VERITAS data analysis, IACT cross-normalisation analysis, and paper writing; S. Jorstad: Swift-UVOT data analysis; A. Kaur: Swift-XRT data analysis; T. Kawashima: SED Modelling and interpretation; S. Kerby: Swift-XRT data analysis; J.-Y. Kim: KVN data analysis; M. Kino: SED modelling and interpretation; E. V. Kravchenko: VLBA data analysis; S.-S. Lee: KVN data analysis; R.-S. Lu: GMVA data analysis; S. Marko ff : MWL results interpretation and paper writing ; D. Mazin: MAGIC data analysis; J. Michail: paper writing; J. Neilsen: Chandra and NuSTAR data analysis and X-ray results interpretation; M. A. Nowak: Swift-XRT analysis; G. Pühlhofer: results interpretation and IACT cross-normalisation analysis; V. Ramakrishnan: paper writing; B. Ripperda: MWL results interpretation and paper writing; M. Santander: VERITAS data analysis; M. Sasada: SED modelling; S. S. Savchenko: SwiftUVOT analysis crosscheck; C. Sheridan: paper writing; all remaining authors: contributed with technical tasks, development, maintenance, and operations of the instruments as well as with reviewing the draft.  \nThe Event Horizon Telescope Collaboration thanks the following organisations and programs: the Academia Sinica; the Academy of Finland (projects 274477, 284495, 312496, 315721); the Agencia Nacional de Investigación y Desarrollo (ANID), Chile via NCN19\\_058 (TITANs), Fondecyt 1221421 and BASAL FB210003; the Alexander von Humboldt Stiftung; an Alfred P. Sloan Research Fellowship; Allegro, the European ALMARegional Centre node in the Netherlands, the NL astronomy research network NOVA and the astronomy institutes of the University of Amsterdam, Leiden University, and Radboud University; the ALMA North America Development Fund; the Astrophysics and High Energy Physics programme by MCIN (with funding from European Union NextGenerationEU, PRTRC17I1); the Black Hole Initiative, that is funded by grants from the John Templeton Foundation (60477, 61497, 62286) and the Gordon and Betty Moore Foundation (Grant GBMF-8273) although the opinions expressed in this work are those of the author and do not necessarily reflect the views of these Foundations; the Brinson Foundation; \'la Caixa\' Foundation (ID 100010434) through fellowship codes LCF / BQ / DI22 / 11940027 and LCF / BQ / DI22 / 11940030; Chandra DD7-18089X and TM6-17006X; the China Scholarship Council; the China Postdoctoral Science Foundation fellowships (2020M671266, 2022M712084); Consejo Nacional de Humanidades, Ciencia y Tecnología (CONAHCYT, Mexico, projects U0004-246083, U0004-259839, F0003-272050, M0037-279006, F0003-  \n281692, 104497, 275201, 263356); the Colfuturo Scholarship; the Consejería de Economía, Conocimiento, Empresas y Universidad of the Junta de Andalucía (grant P18-FR-1769), the Consejo Superior de Investigaciones Científicas (grant 2019AEP112); the Delaney Family via the Delaney Family John A. Wheeler Chair at Perimeter Institute; Dirección General de Asuntos del Personal Académico-Universidad Nacional Autónoma de México (DGAPA-UNAM, projects IN112820 and IN108324); the Dutch Organization for Scientific Research (NWO) for the VICI award (grant 639.043.513), the grant OCENW.KLEIN.113, and the Dutch Black Hole Consortium (with project No. NWA 1292.19.202) of the research programme the National Science Agenda; the Dutch National Supercomputers, Cartesius and Snellius (NWO grant 2021.013); the EACOA Fellowship awarded by the East Asia Core Observatories Association, which consists of the Academia Sinica Institute of Astronomy and Astrophysics, the National Astronomical Observatory of Japan, Center for Astronomical Mega-Science, Chinese Academy of Sciences, and the Korea Astronomy and Space Science Institute; the European Research Council (ERC) Synergy Grant \'BlackHoleCam: Imaging the Event Horizon of Black Holes" (grant 610058); the European Union Horizon 2020 research and innovation programme under grant agreements RadioNet (No. 730562) and M2FINDERS (No. 101018682); the European Research Council for advanced grant \'JETSET: Launching, propagation and emission of relativistic jets from binary mergers and across mass scales\' (grant No. 884631); the Horizon ERC Grants 2021 programme under grant agreement No. 101040021; the FAPESP (Fundação de Amparo á Pesquisa do Estado de São Paulo) under grant 2021 / 01183-8; the Fondo CAS-ANID folio CAS220010; the Generalitat Valenciana (grants APOSTD / 2018 / 177 and ASFAE / 2022 / 018) and GenT Program (project CIDEGENT / 2018 / 021); the Gordon and Betty Moore Foundation (GBMF-3561, GBMF-5278, GBMF-10423); the Institute for Advanced Study; the Istituto Nazionale di Fisica Nucleare (INFN) sezione di Napoli, iniziative specifiche TEONGRAV; the International Max Planck Research School for Astronomy and Astrophysics at the Universities of Bonn and Cologne; DFG research grant \'Jet physics on horizon scales and beyond\' (grant No. 443220636); Joint Columbia / Flatiron Postdoctoral Fellowship (research at the Flatiron Institute is supported by the Simons Foundation); the Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT; grant JPMXP1020200109); the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for JSPS Research Fellowship (JP17J08829); the Joint Institute for Computational Fundamental Science, Japan; the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS, grants QYZDJSSW-SLH057, QYZDJSSW-SYS008, ZDBS-LY-SLH011); the Leverhulme Trust Early Career Research Fellowship; the Max-Planck-Gesellschaft (MPG); the Max Planck Partner Group of the MPG and the CAS; the MEXT / JSPS KAKENHI (grants 18KK0090, JP21H01137, JP18H03721, JP18K13594, 18K03709, JP19K14761, 18H01245, 25120007, 19H01943, 21H01137, 21H04488, 22H00157, 23K03453); the MICINN Research Project PID2019-108995GB-C22; the MIT International Science and Technology Initiatives (MISTI) Funds; the Ministry of Science and Technology (MOST) of Taiwan (103-2119-M-001-010-MY2, 105-2112-M-001-025-MY3, 1052119-M-001-042, 106-2112-M-001-011, 106-2119-M-001-013, 106-2119-M-001-027, 106-2923-M-001-005, 107-2119M-001-017, 107-2119-M-001-020, 107-2119-M-001-041, 107-2119-M-110-005, 107-2923-M-001-009, 108-2112-M001-048, 108-2112-M-001-051, 108-2923-M-001-002, 109-  \n2112-M-001-025, 109-2124-M-001-005, 109-2923-M-001-001, 110-2112-M-001-033, 110-2124-M-001-007, 110-2923-M-001001, and 112-2112-M-003-010-MY3); the National Science and Technology Council (NSTC) of Taiwan (111-2124-M-001-005, 112-2124-M-001-014); the Ministry of Education (MoE) of Taiwan Yushan Young Scholar Program; the Physics Division, National Center for Theoretical Sciences of Taiwan; the National Aeronautics and Space Administration (NASA, Fermi Guest Investigator grant 80NSSC23K1508, NASA Astrophysics Theory Program grant 80NSSC20K0527, NASA NuSTAR award 80NSSC20K0645); NASA Hubble Fellowship grants HST-HF2-51431.001-A, HST-HF2-51482.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555; the National Institute of Natural Sciences (NINS) of Japan; the National Key Research and Development Program of China (grant 2016YFA0400704, 2017YFA0402703, 2016YFA0400702); the National Science and Technology Council (NSTC, grants NSTC 111-2112-M-001 -041, NSTC 111-2124-M-001-005, NSTC 112-2124-M-001-014); the US National Science Foundation (NSF, grants AST-0096454, AST-0352953, AST-0521233, AST-0705062, AST-0905844, AST-0922984, AST-1126433, OIA-1126433, AST-1140030, DGE-1144085, AST-1207704, AST-1207730, AST-1207752, MRI-1228509, OPP-1248097, AST-1310896, AST-1440254, AST-1555365, AST-1614868, AST-1615796, AST-1715061, AST-1716327, AST-1726637, OISE-1743747, AST-1743747, AST-1816420, AST-1935980, AST-1952099, AST-2034306, AST-2205908, AST-2307887); NSF Astronomy and Astrophysics Postdoctoral Fellowship (AST-1903847); the Natural Science Foundation of China (grants 11650110427, 10625314, 11721303, 11725312, 11873028, 11933007, 11991052, 11991053, 12192220, 12192223, 12273022, 12325302); the Natural Sciences and Engineering Research Council of Canada (NSERC, including a Discovery Grant and the NSERC Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program); the National Research Foundation of Korea (the Global PhD Fellowship Grant: grants NRF-2015H1A2A1033752, the Korea Research Fellowship Program: NRF-2015H1D3A1066561, Brain Pool Program: 2019H1D3A1A01102564, Basic Research Support Grant 2019R1F1A1059721, 2021R1A6A3A01086420, 2022R1C1C1005255, 2022R1F1A1075115); Netherlands Research School for Astronomy (NOVA) Virtual Institute of Accretion (VIA) postdoctoral fellowships; NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation; Onsala Space Observatory (OSO) national infrastructure, for the provisioning of its facilities / observational support (OSO receives funding through the Swedish Research Council under grant 2017-00648); the Perimeter Institute for Theoretical Physics (research at Perimeter Institute is supported by the Government of Canada through the Department of Innovation, Science and Economic Development and by the Province of Ontario through the Ministry of Research, Innovation and Science); the Princeton Gravity Initiative; the Spanish Ministerio de Ciencia e Innovación (grants PGC2018-098915-B-C21, AYA2016-80889-P, PID2019-108995GB-C21, PID2020-117404GB-C21); the University of Pretoria for financial aid in the provision of the new Cluster Server nodes and SuperMicro (USA) for a SEEDING GRANT approved toward these nodes in 2020; the Shanghai Municipality orientation program of basic research for international scientists (grant no. 22JC1410600); the Shanghai  \nPilot Program for Basic Research, Chinese Academy of Science, Shanghai Branch (JCYJ-SHFY-2021-013); the State Agency for Research of the Spanish MCIU through the \'Center of Excellence Severo Ochoa\' award for the Instituto de Astrofísica de Andalucía (SEV-2017- 0709); the Spanish Ministry for Science and Innovation grant CEX2021-001131-S funded by MCIN / AEI / 10.13039 / 501100011033; the Spinoza Prize SPI 78-409; the South African Research Chairs Initiative, through the South African Radio Astronomy Observatory (SARAO, grant ID 77948), which is a facility of the National Research Foundation (NRF), an agency of the Department of Science and Innovation (DSI) of South Africa; the Swedish Research Council (VR); the Taplin Fellowship; the Toray Science Foundation; the UK Science and Technology Facilities Council (grant no. ST / X508329 / 1); the US Department of Energy (USDOE) through the Los Alamos National Laboratory (operated by Triad National Security, LLC, for the National Nuclear Security Administration of the USDOE, contract 89233218CNA000001); the YCAA Prize Postdoctoral Fellowship; and Conicyt through Fondecyt Postdoctorado (project 3220195).  \nWe thank the sta ff at the participating observatories, correlation centres, and institutions for their enthusiastic support. This paper makes use of the following ALMA data: ADS / JAO.ALMA#2016.1.01154.V and ADS / JAO.ALMA#2017.1.00841.V. ALMA is a partnership of the European Southern Observatory (ESO; Europe, representing its member states), NSF, and National Institutes of Natural Sciences of Japan, together with National Research Council (Canada), Ministry of Science and Technology (MOST; Taiwan), Academia Sinica Institute of Astronomy and Astrophysics (ASIAA; Taiwan), and Korea Astronomy and Space Science Institute (KASI; Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, Associated Universities, Inc. (AUI) / NRAO, and the National Astronomical Observatory of Japan (NAOJ). The NRAO is a facility of the NSF operated under cooperative agreement by AUI. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the O ffi ce of Science of the U.S. Department of Energy under contract No. DE-AC0500OR22725; the ASTROVIVES FEDER infrastructure, with project code IDIFEDER-2021-086; the computing cluster of Shanghai VLBI correlator supported by the Special Fund for Astronomy from the Ministry of Finance in China; We also thank the Center for Computational Astrophysics, National Astronomical Observatory of Japan. This work was supported by FAPESP (Fundacao de Amparo a Pesquisa do Estado de Sao Paulo) under grant 2021 / 01183-8.  \nAPEXis a collaboration between the Max-Planck-Institut für Radioastronomie (Germany), ESO, and the Onsala Space Observatory (Sweden). The SMA is a joint project between the SAO and ASIAA and is funded by the Smithsonian Institution and the Academia Sinica. The JCMT is operated by the East Asian Observatory on behalf of the NAOJ, ASIAA, and KASI, as well as the Ministry of Finance of China, Chinese Academy of Sciences, and the National Key Research and Development Program (No. 2017YFA0402700) of China and Natural Science Foundation of China grant 11873028. Additional funding support for the JCMT is provided by the Science and Technologies Facility Council (UK) and participating universities in the UK and Canada. The LMT is a project operated by the Instituto Nacional de Astrófisica, Óptica, y Electrónica (Mexico) and the University of Massachusetts at Amherst (USA). The IRAM 30mtelescope on Pico Veleta, Spain is operated by IRAM and sup-  \ned by CNRS (Centre National de la Recherche Scientifique, France), MPG (Max-Planck-Gesellschaft, Germany), and IGN (Instituto Geográfico Nacional, Spain). The SMT is operated by the Arizona Radio Observatory, a part of the Steward Observatory of the University of Arizona, with financial support of operations from the State of Arizona and financial support for instrumentation development from the NSF. Support for SPT participation in the EHT is provided by the National Science Foundation through award OPP-1852617 to the University of Chicago. Partial support is also provided by the Kavli Institute of Cosmological Physics at the University of Chicago. The SPT hydrogen maser was provided on loan from the GLT, courtesy of ASIAA.  \nThis work used the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant ACI1548562, and CyVerse, supported by NSF grants DBI-0735191, DBI-1265383, and DBI-1743442. XSEDE Stampede2 resource at TACC was allocated through TG-AST170024 and TGAST080026N. XSEDE JetStream resource at PTI and TACC was allocated through AST170028. This research is part of the Frontera computing project at the Texas Advanced Computing Center through the Frontera Large-Scale Community Partnerships allocation AST20023. Frontera is made possible by National Science Foundation award OAC-1818253. This research was done using services provided by the OSG Consortium (Pordes et al. 2007; Sfiligoi et al. 2009), which is supported by the National Science Foundation award Nos. 2030508 and 1836650. Additional work used ABACUS2.0, which is part of the eScience center at Southern Denmark University, and the Kultrun Astronomy Hybrid Cluster (projects Conicyt Programa de Astronomia Fondo Quimal QUIMAL170001, Conicyt PIA ACT172033, Fondecyt Iniciacion 11170268, Quimal 220002). Simulations were also performed on the SuperMUC cluster at the LRZ in Garching, on the LOEWE cluster in CSC in Frankfurt, on the HazelHen cluster at the HLRS in Stuttgart, and on the Pi2.0 and Siyuan Mark-I at Shanghai Jiao Tong University. The computer resources of the Finnish IT Center for Science (CSC) and the Finnish Computing Competence Infrastructure (FCCI) project are acknowledged. This research was enabled in part by support provided by Compute Ontario ( http://computeontario.ca ), Calcul Quebec ( http: //www.calculquebec.ca ), and Compute Canada ( http:// www.computecanada.ca ).  \nThe EHTC has received generous donations of FPGA chips from Xilinx Inc., under the Xilinx University Program. The EHTC has benefited from technology shared under open-source license by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER). The EHT project is grateful to T4Science and Microsemi for their assistance with hydrogen masers. This research has made use of NASA\'s Astrophysics Data System. We gratefully acknowledge the support provided by the extended sta ff of the ALMA, from the inception of the ALMA Phasing Project through the observational campaigns of 2017 and 2018. We would like to thank A. Deller and W. Brisken for EHT-specific support with the use of DiFX. We thank Martin Shepherd for the addition of extra features in the Difmap software that were used for the CLEAN imaging results presented in this paper. We acknowledge the significance that Maunakea, where the SMA and JCMT EHT stations are located, has for the indigenous Hawaiian people.  \nThis research has made use of data obtained with 12 radio telescopes from the East Asian VLBI Network (EAVN): 2 stations from the Chinese VLBI Network (the Tianma radio telescope operated by Shanghai Astronomical Observatory of Chinese Academy of Sciences (CAS) and the Nanshan radio tele-  \nscope operated by Xinjiang Astronomical Observatory of CAS), 2 stations from the Japanese VLBI Network (the Hitachi station operated by Ibaraki University and the Kashima station operated by the National Institute of Information and Communications Technology), the Korean VLBI Network operated by the Korea Astronomy and Space Science Institute (KASI), the VLBI Exploration of Radio Astrometry (VERA) operated by the National Astronomical Observatory of Japan (NAOJ), and the Nobeyama 45-meter radio telescope operated by NAOJ. We thank VERA sta ff members who helped the operation. We are grateful to KVN sta ff who helped operate the array and to correlate the data for quasi-simultaneous multi-wavelength observations of M87. The KVN is a facility operated by KASI. The KVN operations are supported by KREONET (Korea Research Environment Open NETwork), which is managed and operated by KISTI (Korea Institute of Science and Technology Information)  \nThe VLBA is an instrument of the National Radio Astronomy Observatory. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated by Associated Universities, Inc.  \nThe Fermi LAT Collaboration acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis. These include the National Aeronautics and Space Administration and the Department of Energy in the United States, the Commissariat à l\'Energie Atomique and the Centre National de la Recherche Scientifique / Institut National de Physique Nucléaire et de Physique des Particules in France, the Agenzia Spaziale Italiana and the Istituto Nazionale di Fisica Nucleare in Italy, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), High Energy Accelerator Research Organization (KEK) and Japan Aerospace Exploration Agency (JAXA) in Japan, and the K. A. Wallenberg Foundation, the Swedish Research Council and the Swedish National Space Board in Sweden. Additional support for science analysis during the operations phase is gratefully acknowledged from the Istituto Nazionale di Astrofisica in Italy and the Centre National d\'Etudes Spatiales in France. This work is performed in part under DOE Contract DE-AC02-76SF00515.  \nThe support of the Namibian authorities and of the University of Namibia in facilitating the construction and operation of H.E.S.S. is gratefully acknowledged, as is the support by the German Ministry for Education and Research (BMBF), the Max Planck Society, the German Research Foundation (DFG), the Helmholtz Association, the Alexander von Humboldt Foundation, the French Ministry of Higher Education, Research and Innovation, the Centre National de la Recherche Scientifique (CNRS / IN2P3 and CNRS / INSU), the Commissariat à l\'énergie atomique et aux énergies alternatives (CEA), the U.K. Science and Technology Facilities Council (STFC), the Knut and Alice Wallenberg Foundation, the National Science Centre, Poland grant no. 2016 / 22 / M / ST9 / 00382, the South African Department of Science and Technology and National Research Foundation, the University of Namibia, the National Commission on Research, Science & Technology of Namibia (NCRST), the Austrian Federal Ministry of Education, Science and Research and the Austrian Science Fund (FWF), the Australian Research Council (ARC), the Japan Society for the Promotion of Science and by the University of Amsterdam. We appreciate the excellent work of the technical support sta ff in Berlin, Zeuthen, Heidelberg, Palaiseau, Paris, Saclay, Tübingen and in Namibia in the construction and operation of the equipment. This work benefited from services provided by the H.E.S.S. Virtual Organisa-  \n, supported by the national resource providers of the EGI Federation.  \nWe would like to thank the Instituto de Astrofísica de Canarias for the excellent working conditions at the Observatorio del Roque de los Muchachos in La Palma. The financial support of the German BMBF, MPG and HGF; the Italian INFN and INAF; the Swiss National Fund SNF; the grants PID2019-104114RB-C31, PID2019-104114RB-  \nC32, PID2019-104114RB-C33,  \nPID2019-105510GB-  \nC31, PID2019-107847RB-C41,  \nPID2019-107847RB-  \nC42,  \nPID2019-107847RB-C44,  \nPID2019-107988GB-  \nC22, PID2022-136828NB-C41,  \nPID2022-137810NB-  \nC22, PID2022-138172NB-C41,  \nPID2022-138172NB-  \nC42,  \nPID2022-138172NB-C43,  \nPID2022-139117NB-  \nC41, PID2022-139117NB-C42, PID2022-139117NB-C43, PID2022-139117NB-C44 funded by the Spanish MCIN / AEI / 10.13039 / 501100011033 and \'ERDF A way of making Europe\'; the Indian Department of Atomic Energy; the Japanese ICRR, the University of Tokyo, JSPS, and MEXT; the Bulgarian Ministry of Education and Science, National RI Roadmap Project DO1-400 / 18.12.2020 and the Academy of Finland grant nr. 320045 is gratefully acknowledged. This work was also been supported by Centros de Excelencia \'Severo Ochoa\' y Unidades \'María de Maeztu\' program of the Spanish MCIN / AEI / 10.13039 / 501100011033 (CEX2019-000920-S, CEX2019-000918-M, CEX2021-001131-S) and by the CERCA institution and grants 2021SGR00426 and 2021SGR00773 of the Generalitat de Catalunya; by the Croatian Science Foundation (HrZZ) Project IP-2022-10-4595 and the University of Rijeka Project uniri-prirod-18-48; by the Deutsche Forschungsgemeinschaft (SFB1491) and by the Lamarr-Institute for Machine Learning and Artificial Intelligence; by the Polish Ministry Of Education and Science grant No. 2021 / WK / 08; and by the Brazilian MCTIC, CNPq and FAPERJ.  \nThe Medicina and Noto radio telescopes are funded by the Ministry of University and Research (MUR) and are operated as National Facility by the National Institute for Astrophysics (INAF).  \nVERITAS is supported by grants from the U.S. Department of Energy O ffi ce of Science, the U.S. National Science Foundation and the Smithsonian Institution, by NSERC in Canada, and by the Helmholtz Association in Germany. This research used resources provided by the Open Science Grid, which is supported by the National Science Foundation and the U.S. Department of Energy\'s O ffi ce of Science, and resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy O ffi ce of Science User Facility operated under Contract No. DE-AC02-05CH11231. We acknowledge the excellent work of the technical support sta ff at the Fred Lawrence Whipple Observatory and at the collaborating institutions in the construction and operation of the instrument.  \nS.M. is thankful for support from an NWO (Netherlands Organisation for Scientific Research) VICI award, grant Nr. 639.043.513.  \nD.H. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant and the Canada Research Chairs program.  \nG.P. acknowledges support by ICSC - Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by European Union - NextGenerationEU.  \nJ.-C.A. acknowledges support from the Malaysian Fundamental Research Grant Scheme (FRGS) FRGS / 1 / 2019 / STG02 / UM / 02 / 6.  \nArticle number, page 34 of 44  \nM.B. acknowledges support from the YCAA Prize Postdoctoral Fellowship and the Black Hole Initiative at Harvard University, which is funded in part by the Gordon and Betty Moore Foundation (grant GBMF8273) and in part by the John Templeton Foundation.  \nK.H. acknowledges support from JSPS KAKENHI grant Nos. JJP18H03721, JP19H01943, and JP18KK0090.  \nT.K. acknowledges support from JSPS KAKENHI grant Nos. JP18K13594, JP19H01908, JP19H01906, JP23K03448, JP23H00117, MEXT as \'Program for Promoting Researches on the Supercomputer Fugaku\' (Structure and Evolution of680 the Universe Unraveled by Fusion of Simulation and AI;681 Grant Number JPMXP1020230406; Project ID hp230204 , hp240219), by the HPCI System Research Project (Project ID: hp230116, hp240054), and JICFuS. A part of the calculations were carried out on the XC50 at the Center for Computational Astrophysics, National Astronomical Observatory of Japan.  \nR.-S.L. is supported by the National Science Fund for Distinguished Young Scholars of China (Grant No. 12325302), the Key Program of the National Natural Science Foundation of China (NSFC, grant No. 11933007), the Key Research Program of Frontier Sciences, CAS (grant No. ZDBS-LY-SLH011), the Shanghai Pilot Program for Basic Research, CAS, Shanghai Branch (JCYJ-SHFY-2021-013) and the Max Planck Partner Group of the MPG and the CAS.  \nJ.N. acknowledges support from SAO award DD7-18089X and NASA award 80NSSC20K0645.  \nJ.P. acknowledges financial support from the Korean National Research Foundation (NRF) via Global PhD Fellowship grant 2014H1A2A1018695 and support through the EACOA Fellowship awarded by the East Asia Core Observatories Association, which consists of the Academia Sinica Institute of Astronomy and Astrophysics, the National Astronomical Observatory of Japan, Center for Astronomical Mega-Science, Chinese Academy of Sciences, and the Korea Astronomy and Space Science Institute.  \nY.-Z.C. acknowledges support from the Natural Science Foundation of China (grant 12303021) and the China Postdoctoral Science Foundation (No. 2024T170845).  \nJ.Y.K. is supported for this research by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT; grant no. 2022R1C1C1005255).  \nJ.M. is supported by an NSF Astronomy and Astrophysics Postdoctoral Fellowship under award AST-2401752.', 'Appendix B: Detailed analysis descriptions': 'Appendix B.1: Radio observations  \nAppendix B.1.1: VERA 22 GHz  \nAll the data were analysed in with standard VERA data reduction procedures (see Nagai et al. 2013; Hada et al. 2014, for more details). We note that VERA recovers only part of the extended jet emission due to the lack of short baselines, so the total VERA fluxes listed in Table C.1 significantly underestimate the actual total jet fluxes.', 'Appendix B.1.2: EAVN/KaVA 22 and 43 GHz': 'Especially between March and May of 2017 and 2018 when EHT M87 observations were performed, the observing cadence of EAVN monitoring was intensified. The default array  \nconfigurations were KVN + VERA + Tianma + Nanshan + Ibaraki at 22 GHz and KVN + VERA + Tianma at 43 GHz respectively, while occasionally a few more stations in East Asia (Sejong, Kashima, Nobeyama) and Italy (Medicina, Sardinia) joined if available. During the period outside March-May of each year, the observations were mostly conducted with the KVN and VERA Array (KaVA; Niinuma et al. 2014; Hada et al. 2017; Park et al. 2019), a core array of EAVN.  \nEach of the KaVA / EAVN sessions was made in a 5-7-hour continuous run at a data recording rate of 1 Gbps (a total bandwidth of 256 MHz in a single polarisation mode). All the data were correlated at the Daejeon hardware correlator installed at KASI. All the EAVN data were calibrated in the standard manner of VLBI data reduction procedures. We used the AIPS (Greisen 2003) software package for the initial calibration of visibility amplitude, bandpass and phase calibration. Imaging with the CLEAN algorithm (Högbom 1974) and self-calibration were performed with the Difmap (Shepherd 1997) software.', 'Appendix B.1.3: VLBA 24 and 43 GHz': 'The total on-source time during the full-track segments is about 1.7 hours at 24 GHz and 6 hours at 43 GHz, while for the short segments it amounts to 30 minutes at 43 GHz. The sources OJ 287 and 3C 279 were observed to use as fringe finders and bandpass calibrators. In each band eight 32 MHz-wide frequency channels were recorded in both right and left circular polarisations at a rate of 2048 Mbps, and correlated with the VLBA software correlator in Socorro (Deller et al. 2011). The initial data reduction was conducted using AIPS , following the standard calibration procedures for VLBI data (Crossley et al. 2012; Walker 2014). Deconvolution and self-calibration algorithms, implemented in Difmap , were used for phase and amplitude calibration and for constructing the final images. Amplitude calibration accuracy of 10% is adopted for both frequencies. Images and polarimetric results from part of these VLBA observations are published in Kravchenko et al. (2020) and Park et al. (2021).', 'Appendix B.1.4: GMVA+ALMA 86GHz': 'The participating GMVA stations include the eight VLBA antennas equipped with 3.5 mm receivers, the E ff elsberg 100 m telescope, the 100 m Green Bank Telescope, the Metsähovi 14 m telescope, the Onsala 20 m telescope, the IRAM 30 m telescope, and the Yebes 40m telescope (YS). We note that the participation of ALMA and the GLT has improved the north-south resolution by a factor of ∼ 4 for the observations of M87.  \nThe observations (GMVA project code ML005) were performed for a total of ∼ 13 h ( ∼ 6 h with five telescopes in Europe (E ff elsberg, Metstähovi, Onsala, IRAM30m and Yebes), ∼ 7 h with ALMA, VLBA and GBT, and ∼ 12 h with the GLT). Two bright calibration sources (3C273 and 3C279) were observed every 20-40 minutes in interleaved scans. The data were correlated with the VLBI correlator at the Max Planck Institute for Radio Astronomy using DiFX (Deller et al. 2011).  \nDuring these observations, most stations recorded the signals on a circular polarisation basis. However, the phased ALMA recorded the signals on a linear polarisation basis. Consequently, the visibilities between ALMA and the other stations were correlated on a mixed polarisation basis. After correlation, these visibilities were converted into a pure circular basis using the internal calibration of the ALMA interferometric data with the PolConvert algorithm (Martí-Vidal et al. 2016). In addition,  \nGLT participated in these observations on a best-e ff ort basis during its commissioning phase. The waveguide phase-shifter of the GLT that was used to convert the on-sky circular-polarisation signals into linear polarisation for detection at the backend was erroneously configured to apply a rotation of 45 · (instead of 90 · ) between the polarisation channels. The instrumental polarisation e ff ects caused by this rotation were removed with a dedicated algorithm after correlation. After this instrumental polarisation calibration, the data were then further calibrated following typical VLBI data reduction procedures in AIPS . Hybrid imaging was performed in the Difmap software with CLEAN and selfcalibration. We refer the reader to Lu et al. (2023) for further details of data calibration. Based on the calibration and imaging of the calibrator 3C273, we consider an error budget of 25 % for the flux density estimate of M87. While a dedicated study based on these data is presented in Lu et al. (2023), here we provide a peak flux density of the core and VLBI-scale total flux density (Tables C.1 and C.2).', 'Appendix B.1.5: KVN 22, 43, 86, and 129 GHz': 'The data were calibrated using a modified version of the KVN pipeline (Hodgson et al. 2016) to include ionospheric delay corrections with total electron content (TEC) maps, parallactic angle corrections, and Earth orientation parameter corrections. Prior to global fringe fitting, the 43, 86, and 129 GHz data were calibrated with the 22 GHz data by means of frequency phase transfer. After the pipeline calibration with AIPS , the inner 75% of each subband (due to flux loss at the bandpass edges) was used for further analysis. Following Kim et al. (2018), the data were averaged by 30 seconds for the 22 GHz and 43 GHz data, and by 10 seconds for the 86 GHz and 129 GHz data. As in M87MWL2017, we assume a flux density measurement uncertainty of 10% at 43 GHz and 86 GHz, and 30% at 129 GHz. During the observations, a phase instability issue occurred with the 22 GHz receiver at the KVN Yonsei site (KYS) between January and March 2018, and between November 2018 and February 2019. This resulted in a ∼ 50% flux loss for all KYS baselines. From a sample of compact sources within the iMOGABA program, we derived and applied empirical correction factors to the impacted data. An uncertainty of 15% is assumed for the 22 GHz flux density measurements made during observations where the empirical corrections were applied. For observations that were not impacted by the flux loss, an uncertainty of 10% is adopted. A common 1 mas FWHM circular Gaussian beam is used to convolve the 22, 43, 86, 129 GHz CLEAN maps when determining the peak flux density of the core. This is closest to the natural beams of the KVN at 86 and 129 GHz. In Table C.1, we summarise the data obtained during 2018. In Fig.14, we also add the earlier (2012-2016) data that were originally published in the literature (Kim et al. 2018) but the peak flux densities are recalculated using the procedures applied for 2017-2018 data.', 'Appendix B.1.6: Global 43 GHz VLBI': 'The participating stations include E ff elsberg, Yebes 40 m, Metsahovi, KVN (three stations), 100-m Green Bank Telescope, VLBA (10 stations), and the phased Jansky Very Large Array (JVLA). The data were recorded with a total recording rate of 2 Gbps, corresponding to 256 MHz bandwidth per polarisation. The scheduling, observation, and data reduction followed the standard procedures using AIPS . Similar to other VLBI data reduction and imaging, the CLEAN imaging and self-calibration  \nwere made using procedures implemented in Difmap . The resulting FWHM angular resolutions are 0 . 2 × 0 . 7 (0 . 12 × 0 . 47) mas 2 with the beam position angle ∼ 0 deg for the natural (uniform) weighting.', 'Appendix B.1.7: ALMA 93 GHz and 221 GHz': "The VLBI observations were carried out while the array was in its most compact configuration and only antennas within a radius of 180-m (from the array centre) were used for phasing. The observations were performed in full-polarisation mode to supply the inputs to the polarisation conversion process at the VLBI correlators (Martí-Vidal et al. 2016). The setup included four spectral windows (SPWs) of 1875 MHz in each band, two in the lower and two in the upper sideband, centred at 86, 88, 98, and 100 GHz in Band 3, and 213, 215, 227, and 229 GHz in Band 6, respectively. The SPWs were correlated with 240 channels per SPW (corresponding to a spectral resolution of 7.8125 MHz). Details about ALMA operations in VLBI mode and a full description of the data processing and calibration can be found in Goddi et al. (2019).  \nImaging was performed with the Common Astronomy Software Applications package ( CASA , version 6.4.1 used; McMullin et al. 2007; CASA Team et al. 2022) task tclean . Only phased antennas were used to produce the final images (with baselines < 360 m), yielding synthesised beam sizes ∼ 2.5 '' in Band 3 and ∼ 1 '' in Band 6, respectively. To isolate the core emission from the jet, we compute the sum of the central nine pixels of the model (cleaning component) map, an area of 3 × 3 pixels; the contribution from the jet is accounted for by also summing the clean components along the jet. While the extended emission accounts for less than 20% of the total emission at 1.3 mm, at 3mm it also includes emission from the radio lobes, accounting for more than 60% of the total emission. The absolute amplitude scale calibration has a 5% / 10% uncertainty in Band 3 / 6, respectively. Details about the imaging and flux extraction methods can be found in Goddi et al. (2021).", 'Appendix B.1.8: SMA 200 & 345 GHz': "Data from this program are updated regularly and are available at the SMA Observer Center website (SMAOC 7 ). Data were primarily obtained in a compact configuration (baselines from 10 to 75 m) though some were obtained at longer baselines up to 210 m. The e ff ective spatial resolution was therefore generally around 3 '' . We have limited analysis to projected baselines longer than 30 k λ (e ff ective spatial scales smaller than 6.8 '' ). The flux density was obtained by vector averaging of the calibrated visibility data from each observation.  \nInterferometric observations of M87 were additionally conducted as part of the 2017 and 2018 EHT campaigns, concurrent with the SMA running in phased-array mode operating at 230 GHz. In 2017 the SMA was in compact configuration, while in 2018 the SMA was in extended configuration (baselines 25 m to 210 m). In both years, the interferometer operated in dualpolarisation mode. The SMA correlator produces four separate but contiguous 2 GHz spectral windows per sideband, resulting in frequency coverage of 208 -216 and 224 -232 GHz. Data were passband and amplitude calibrated using 3C 279, with flux calibration performed using either Callisto, Ganymede, or Titan. Phase calibration was done through self-calibration of the M87 data itself, assuming a point-source model.", 'Appendix B.2.1: Optical observations by the Kanata telescope': 'We adopted standard reduction procedures for the images: biasor dark-frame subtraction, correction of bad pixels, flattening correction, removal of contamination of CR signals, skybackground subtraction, solving the corresponding coordinates in the World Coordinate System (WCS) using field stars, and stacking of the images aligned with the coordinates of WCS.  \nWe performed an aperture photometry of field stars in the stacked images using Source Extractor software (Bertin & Arnouts 1996). The value of FLUX\\_AUTO was adopted as the photometric measurements. We estimated zero points of the images in the AB magnitude system using nearby stars whose actual magnitudes have been provided in the references: the PanSTARRS1 catalogue (Flewelling et al. 2020) of the optical band after conversion of the filter system (Tonry et al. 2012) and the 2MASS catalogue (Skrutskie et al. 2006) of the near-infrared band.', 'Appendix B.2.2: Optical and UV observations from Swift -UVOT': "UVOT data in all filters were analysed with the uvotimsum and uvotsource tasks included in the HEASoft package (v6.32) and the 20231208 CALDB-UVOTA release. Source counts were extracted from a circular region of 5 '' radius centred on the source, while background counts were derived from a circular region with 30 '' radius in a source-free region located outside the host galaxy radius ( ∼ 120 '' ). The UVOT magnitudes are corrected for the host galaxy contamination (see Table C.6 ) and the Galactic extinction using an E(B-V) value of 0.020 from Schlafly & Finkbeiner (2011) and the extinction laws from Cardelli et al. (1989) and converted to flux densities using the conversion factors from Breeveld et al. (2010). We have used the same host galaxy model as presented in Table A2 of EHT MWL Science Working Group et al. (2021). However, we have to note that flux densities of the host galaxy listed in Table A2 of EHT MWL Science Working Group et al. (2021) did not have aspect corrections although the correct values of the host galaxy as given in Table C.6 were used in the data reduction of Swift -UVOT data in EHT MWL Science Working Group et al. (2021).", 'Appendix B.2.3: Far-UV observations from AstroSat-UVIT': "For the AstroSat-UVIT analysis, the VIS data are used for drift correction of the images in NUV and FUV channels. The FUV and NUV detectors work in photon-counting mode and the VIS detector works in integration mode. Each of these channels is equipped with a number of narrow and wide band filters (for details refer to Tandon et al. 2017; Tandon et al. 2020). Out of the two AstroSat pointing observations (i.e. A04\\_115T02\\_9000001900 and A04\\_115T02\\_9000002042), only the UVIT data of the second one are available. These observations are made in the BaF2 band (F154W: λ e f f ∼ 1541 Å, with ∆ λ e f f ∼ 380 Å). The level-1 data, downloaded from the AstroSat data archive 'astrobrowse', is used to create the image using the CCDLAB software package (Postma & Leahy 2017) incorporating the corrections for the spacecraft drift, geometrical distortions and flat-fielding.  \nA two-dimensional (2D) image decomposition method was used with IMFIT 8 (Erwin 2015) tool. The following combination of 2D models is used to create the model image of M87 using UVIT: PointSource + PointSource + Core-Sersic + SersicJet + Flat-Sky . The point sources used here are convolved with the PSF of the UVIT instrument.  \nThe UVIT-BaF2 image, centred at M87 and zooming into a small region around it is shown in Fig. B.1. The two point source models are added to account for the contributions from M87*'s core and the HST-1 knot. The Core-Sersic model (Graham et al. 2004) is used to represent the emission from the host galaxy. In order to take the asymmetric jet into account we created a modified version of the Sersic model by adding an asymmetry term to the originally symmetric Sersic function. But even with the added asymmetry, the shape of the bright spots towards the end of the jet is too complex to develop a good analytical model to describe it. Motivated by the above we masked this irregular complex part of the jet with a rectangular mask. The Flat-Sky model (Mondal et al. 2018) was used to include a component for the sky background. The best fit composite model corresponds to a reduced χ 2 value of 1.  \nThis model is then used to represent the composite radial profile for the host and jet. We then performed aperture photometry of the image using the Python package photutils 9 with a source region of 5 '' centred at the position of M87*. The contributions of other prominent components to this region were estimated using the modelled radial profile for composite emission from the jet, the host galaxy and the sky. The corrected magnitude is then converted to flux using an appropriate flux conversion factor as provided by the instrument team. The corresponding numbers are shown in the Table C.8.", 'Appendix B.3.1: Chandra and NuSTAR observations and joint spectral analysis': "We keep the data processing and analysis as consistent as possible with our previous work on the Chandra and NuSTAR data taken in parallel with the 2017 EHT campaign described in M87 MWL2017. We only briefly summarise them here. For the Chandra data, we used standard data reduction procedures in CIAO (Fruscione et al. 2006) version 4.9 to extract 0.4-8 keV spectra from the spatially resolved components, the core, the HST-1 knot, and the outer jet, along with the corresponding instrumental response files. The NuSTAR observation was processed following standard procedures outlined in Perri et al. (2017), using NuSTARDAS version 1.8.0 to produce 3-79 keV spectra and instrumental responses with CALDB version 20180312. Source and background regions for Focal Plane Module A and B (FPMA, FPMB) were the same as in M87MWL2017: 45 '' circles that are large enough to include all of the Chandra spatial components mentioned above as well as much of the surrounding di ff use emission from the intracluster medium (ICM).  \nWe proceed to joint spectral modelling of the Chandra and NuSTAR data in order to take advantage of the high spatial resolution of Chandra and the high-energy sensitivity of NuSTAR . Our modelling strategy, described in detail in M87 MWL2017, properly accounts for the e ff ects of pileup in Chandra data and includes consideration of the cross-normalisation between the  \ninstruments with respect to the modelled di ff use ICM emission that is di ff erently sampled by Chandra and NuSTAR . For the di ff use component, we adopt a spectral model with two APEC components (Smith et al. 2001) with di ff erent temperatures and variable elemental abundances, three additional emission lines, and a cuto ff component for the low-mass X-ray binary contribution from within the host galaxy. For the core, the HST-1 knot, and the remainder of the jet, we modelled their continuum emission using PLs. All spectral models include interstellar absorption (Wilms et al. 2000). The absorption column towards the core was previously found to be higher (e.g. Perlman & Wilson 2005), that we allow for by including an extra layer of absorption in the model for this component.  \nWe performed spectral fitting on spectra binned using a custom algorithm that ensures a minimum signal-to-noise ratio within each energy bin (as described in M87 MWL2017). To find the best-fit model parameters we minimised the Cash statistic (Cash 1979) as appropriate for the low-count regime and our binning strategy. Our best fit has a Cash statistic of 1219.023 for 1034 data bins and 37 free parameters. Our multi-component best-fit model is shown along with the data in Fig. 6; red lines represent the model for each dataset, and the NuSTAR model is the sum of all the other spatial components. We derive uncertainties using a Markov Chain Monte Carlo (MCMC) sampler emcee (Foreman-Mackey et al. 2013) set to run for 60,000 steps with 10 walkers for each of 37 parameters (a total of 22.2 million model evaluations). We discarded the first 185,000 evaluations for calculating chain statistics. Unless otherwise noted, quoted uncertainties represent minimum width intervals containing 90 % of the probability function for each spectral parameter.", 'Appendix B.3.2: Swift -XRT observations and analysis': "The standard pipeline xrtpipeline version 0.13.5 was used to create level 3 cleaned event files for each Swift -XRT observation. We use a circular source region of radius 35 '' for spectral extraction, encompassing all three of the X-ray sources examined individually in the Chandra analysis. For background extraction, we create an annular background region with inner and outer radii of 130 '' and 150 '' , respectively. Using xselect (version 2.4k), we extract spectra for the source and background regions after correcting for pile-up in the source region. If the source region count rate exceeded 0.5 ct s -1 for any observations, we applied a pile-up correction by using an annular region to exclude central pixels a ff ected by pile-up.  \nCreating ancillary response files for each spectrum with the xrtmkarf task, we grouped the spectral files for each epoch to a minimum of 20 counts per bin using the grppha command to facilitate χ 2 fitting using xspec . Since the spectral resolution of Chandra is better than Swift -XRT, we adopted the source and background model fitted in the Chandra analysis. All spectral parameters of that model were frozen at the Chandra best-fit values (see Sect. 2.3.1) except the normalisation of the APEC and line emission backgrounds and the joint normalisation of the three power law components for the core, the HST-1 knot, and jet emission. The relative normalisation between the two APEC background components and between the three PL source components were kept frozen at the Chandra values. We use χ 2 fitting via xspec to conduct this X-ray analysis. In this way, we evaluate the changing total and small-scale components (core, the HST-1 knot and inner jet) fluxes from the source.  \nFig. B.1. AstroSat UVIT observations of M87. Left: M87 in BaF2 band. Right: Di ff erence image of M87. The patched area is basically the masked region of the jet.  \n<!-- image -->", 'Appendix B.3.3: AstroSat-SXT observations and analysis': "We used sxtpipeline (version 1.4b) along with the standard filtering criteria to generate the cleaned events file. The standard procedure includes time-tagging of events, removing hot pixels, bias corrections, coordinate transformation, filtering (South Atlantic Anomaly passage, Sun-angle, Moon-angle), charge transfer ine ffi ciency corrections, and generation of full-frame products. The processed event lists obtained in this way were then input to xselect to extract spectra, light curves, and images.  \nWe used a circular source region of radius of 15 ' centred on the brightest pixel in the SXT image ( α = 12:30:50.41, δ =+ 12:22:32.4), that is consistent with the peak of the flux distribution in Chandra and NuSTAR images. From the PSF size of the SXT we estimate that this region includes over 97 % of the total number of detected photons. The background spectrum extracted using deep blank sky observations and distributed by the instrument team 10 is used to model the spectrum. We used the tool sxtARFModule (version 0.3) along with the full-frame ARF ( sxt\\_pc\\_excl00\\_v04.arf ) distributed by the SXT Payload Operation Center 11 to create ARF for the specific source region selection. We utilised the RMF file sxt\\_pc\\_mat\\_g0to12.rmf corresponding to the default 0-12 grade selection.  \nFor spectral fitting we used a model defined with the following expression in xspec : TBabs × (vvapec + vvapec + cutoffpl + TBabs × powerlaw + powerlaw + powerlaw + Gaussian + vvapec) . This is similar to the model adopted for Chandra and NuSTAR spectral analysis, except one Gaussian component, that is added in order to fit the broad hump-like residuals around 1.4 keV. An additional vvapec component is included to account for the di ff use emission from cluster gas in the outer regions because the SXT samples a larger portion of it (radius ≃ 15 ' ) than other focusing X-ray instruments considered in this work. The parameters of the three vvapec components, except the temperature and normalisation, are fixed to the values obtained from joint Chandra and NuSTAR spectral modelling (Sect. 2.3.1). To reduce the number of free parameters, we tied together the temperature and normalisation parameters of the vvapec components with a multiplicative factor. We adopt the sum of these three components with di ff erent temperatures to represent the large-scale emission from hot gas in the galaxy and the surrounding cluster. Three PL components are representative  \nof the core, the HST-1 knot, and jet contributions; their PL photon indices are kept fixed based on values determined from the analysis of Chandra and NuSTAR data. We keep the normalisation of the core contribution free in the fitting, while the normalisation of the HST-1 knot and jet components is tied with the core normalisation using the multiplicative factors also employed in Swift -XRT spectral modelling in Sect. 2.3.2, as well as in M87 MWL2017.  \nAs in Sect. 2.3.1, we used the Cash statistic to find the bestfitting composite model. To account for the systematics introduced by the procedures related to ARF generation (such as vignetting model and PSF-based scaling of the e ff ective area, etc.), we included an additional 1% uncertainty to the observed spectra. Constraints on the free parameters of the model are given in Table C.9. Figure B.2 shows the AstroSat -SXT individual PL components to be interpreted as upper limits due to the angular resolution of the instrument.", 'Appendix B.4.1: Fermi -LAT observations': "Fermi -LAT (Atwood et al. 2009) is a γ -ray telescope that detects photons by conversion into electron-positron pairs, and it has an operational energy range from 20 MeV to more than 300 GeV. Since its launch in June 2008, it mostly works in survey mode covering the whole sky in about three hours and with a remarkable overall uptime of 99.8% (Ajello et al. 2021).  \nRegarding the data selection, we used P8R3 (v3) SOURCE class events (Bruel et al. 2018), in the energy range between 100 MeV and 1 TeV, in a region of interest (ROI) of 15 · × 15 · centred on the position of M87. The low-energy threshold is motivated by the large uncertainties in the arrival directions of the photons below 100 MeV, leading to possible confusion between point-like sources and the Galactic di ff use component (see Principe et al. (2018) for a di ff erent analysis implementation to solve this and other issues at low energies with Fermi -LAT).  \nThe analysis (that consists of the following steps: model optimisation, source localisation, spectrum and emission variability studies) was performed with Fermipy 12 (v1.0.1; Wood et al. 2017), that uses the Fermi Tools, version 2-0-18. We created counts maps using a pixel size of 0 . 1 · . All γ -rays with zenith angle larger than 95 · were excluded in order to limit the contamination from secondary γ -rays from the Earth's limb (Abdo  \nFig. B.2. AstroSat -SXT observations of M87 with SED estimates from spectral modelling. Dashed-dotted and dotted lines are representative of observations made in February and March 2018, respectively. The grey, light-blue and light-red shaded regions around the lines are 1 σ uncertainty regime derived using parameter estimations, each representing core, jet and the HST-1 knot sub-components, respectively. The downward arrows are indicative that given the limitations of the instrumental capabilities, these estimations should be treated as upper limits for further investigations.  \n<!-- image -->  \net al. 2009). In order to optimise the analysis, we applied different cuts for the data selections at low energies and selected event types with the best PSF 13 . In particular, for energies below 300 MeV, we excluded events with zenith angle larger than 85 · , as well as photons from PSF0 and PSF1 event types. Between 300 MeV and 1 GeV we excluded events with zenith angle larger than 95 · , as well as photons from the PSF0 event type. Above 1 GeV we used all events with zenith angles less than 105 · . The P8R3\\_Source\\_V3 instrument response functions (IRFs) were used. The model used to describe the sky includes all point-like and extended sources located at a distance < 15 · from the source position and listed in the 4FGL-DR2 (Abdollahi et al. 2020) as well as the Galactic di ff use and isotropic emission. For the latter contributions, we used the same templates, 14 adopted to derive the 4FGL catalogue.  \nWe performed the spectral analysis, allowing the di ff use background template normalisation and the spectral parameters of the sources to vary within 3 · from our targets. For the sources in a radius between 3 · -5 · and all variable sources only the normalisation was fit, while we fixed to their 4FGL values the parameters of all the remaining sources within the ROI at larger angular distances from our target. We modelled the spectrum of M87 with a PL function.  \nFinally, for the analysis on the 12 years of LAT data we extracted a light curve using time bins of one month. Similarly, for analysis on a one-month scale centred on EHT observations,  \nthe light curve analysis was repeated using time intervals of two days in order to investigate the emission variability on short time scales. We tried also di ff erent time intervals with both fixed (1 day and 1 week) or customised duration (discriminating between before, during and after the VHE γ -ray flare). The fluxes in each interval were obtained by leaving only the normalisation free to vary and freezing the other spectral parameters to the best-fit values obtained from the full range analysis.", 'Appendix B.4.2: Very high energy observations: H.E.S.S., MAGIC, VERITAS': "H.E.S.S. observations were performed with a wobble mode with an o ff set of 0.7 · from the position of M87, that allows for a simultaneous background estimation. The analysis and reconstruction of the Cherenkov shower images were done with the ImPACT maximum likelihood-based technique (Parsons & Hinton 2014). The separation between γ -rays and hadrons was performed with a boosted decision tree classification method (Ohm et al. 2009). For the signal estimation, the background was estimated using the 'ring-background model' whereas the calculation of the light curve and the spectrum was done using the 'reflected-background model' (Berge et al. 2007). A source with 1% of the Crab Nebula's flux can be detected above 100 GeV in ∼ 10 hours.  \nThe stereoscopic MAGIC observations were conducted in wobble mode with an o ff set of 0.4 · . Image reconstruction and data analysis were performed with the standard MAGIC analysis framework MARS (MAGIC Analysis and Reconstruction Software; Zanin 2013; Aleksi'c et al. 2016). To estimate the instrument's PSF in true energy we used the spatial likelihood analysis package SkyPrism from Vovk et al. (2018). The observations on MJD 58232 were taken with a strong moonlight background present and therefore analysed as described in Ahnen et al. (2017). MAGIC's integral sensitivity is 1% of the Crab Nebula's flux in ∼ 26 hours above an analysis energy threshold of 290 GeV.  \nData analysis of the observations in 2019 was performed identically to the aforementioned 2018 observations. The reconstructed flux levels in 2019 are compatible with the previously observed quiescent flux (see, e.g. MAGIC Collaboration et al. 2020; EHT MWL Science Working Group et al. 2021). The MAGIC fluxes published in MAGIC Collaboration et al. (2020) use an analysis threshold of 300 GeV for the light curve. Instead of scaling the VHE fluxes between 2012 and 2015 according to the PL spectrum, we repeated the flux calculations with a threshold of 350 GeV, using the original high-level analysis files of the authors of MAGIC Collaboration et al. (2020).  \nThe stereoscopic VERITAS observations were conducted in wobble mode with an o ff set of 0.5 · . The data were analysed using the VERITAS standard analysis procedure (Daniel 2008). The main software package used in this work is called Eventdisplay . An overview of this analysis package can be found in Maier & Holder (2018). In addition, a cross-check analysis was performed using an independent software package called VEGAS (Cogan 2007). VERITAS is designed to measure γ rays with energies from ∼ 85 GeV and it is able to detect a source with about 1% of the Crab Nebula within 25 hours (Park 2016).  \nTo explore possible systematic flux scale o ff sets between VERITAS, MAGIC, and H.E.S.S. and investigate how the uncertain flux scales might a ff ect the results in sec. 2.4.2, we applied two cross-normalisation factors ( f 1 and f 2) to the VERITAS and MAGIC fluxes as extra fitting parameters. We note that statistical errors on individual flux points were not altered.  \nTable B.1. Best fit results for a constant and a Gaussian model (Eq. 2.4.2) describing the temporal progression of the flare as seen by VERITAS, MAGIC, and H.E.S.S.  \nWe note that this choice is purely arbitrary and that the crossnormalisation factors could be applied to any two of the three IACTs. For an earlier study of systematic o ff sets between IACTs see Meyer et al. (2010). In our case, the maximum flux scale o ff -sets between the instruments are set to 15%. These o ff sets are used to constrain the fitting range of f 1 and f 2. The results of the fit of a constant and a Gaussian function (Eq. 2.4.2) are shown in Table B.1.", 'Appendix C: Tabulated data': 'This section contains the list of tabulated data from observations described in Sect. 2, as follows:  \n- 1. Summary of radio observations and flux densities at centimetre wavelengths in Table C.1 and millimetre wavelengths in Table C.2;\n- 2. Kanata Telescope flux densities at the optical wavelength of 634.9 nm in Table C.3 and near-infrared wavelength of 1250 nm in Table C.4;\n- 3. Optical and ultraviolet flux densities of Swift-UVOT in Table C.5, while Table C.6 contains of the host galaxy;\n- 4. Photometry at the ultraviolet wavelength of 154 nm from AstroSat-UVIT in Table C.8;\n- 5. Optical HST observations in Table C.7;\n- 6. Parameters of the composite spectral model for AstroSat-SXT data in Table C.9;\n- 7. Summary of Swift -XRT observations and fluxes in the 2-10 keV band in Table C.10;\n- 8. Summary of Chandra core fluxes for the observations performed in 2017 and 2018. in Table C.11, while the Chandra + NuSTAR spectral values for stacked observation, normalised on 2018 emission are reported in Table C.12;\n- 9. Summary of Fermi -LAT fluxes for the customised-bin light curve analysis on April 2018. in Table C.13;\n- 10. Summary of VHE γ -ray observations and fluxes above energies of 350 GeV in Table C.14 and parameters of the spectral models fitted to data from H.E.S.S., MAGIC, and VERITAS in Table C.15;\n- 11. Spectral energy distribution for M87 observed during the 2018 EHT campaign in Table C.1;\n- 12. in addition the spectral models (Model-A-PL, Model-A, BPL, Model-B) used to describe the MWL SED (see Sect. refsec:sedmodel) are also reported as supplementary material.  \nTables C.1 to C.15 are available in electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/ . Furthermore, these data are provided to the community in machinereadable form via the EHTC Data Webpage 15 along with associated documentation and files needed for spectral modelling of the X-ray data.', 'T able C.1. Spectral Ener gy Distrib ution for M87 during the 2018 EHT -MWL campaign.': '<!-- image -->', '2.3.2 . 2.3.2 . 2.3.2 . 2.3.2 . 2.3.2 . 2.3.2 . 2.4.1 . 2.4.1 . 2.4.1 . 2.4.1 . 2.4.1 . 2.4.1 . 2.4.2 . 2.4.2 . 2.4.2 . 2.4.2 . 2.4.2 . 2.4.2 . 2.4.2 . 2.4.2 . 2.4.2 . 2.4.2 . 2.4.2 . 2.4.2 . 2.4.2 . 2.4.2 . 2.4.2 . 2.4.2 .': '1 . 53 ± 0 . 17 1 . 51 ± 0 . 19 1 . 50 ± 0 . 21 1 . 48 ± 0 . 23 1 . 47 ± 0 . 24 1 . 45 ± 0 . 26 < 11 . 6 6 . 88 ± 3 . 01 4 . 24 ± 2 . 20 4 . 08 ± 2 . 37 < 6 . 04 < 5 . 27 2 . 17 ± 0 . 65 2 . 17 ± 0 . 45 1 . 62 ± 0 . 39 1 . 99 ± 0 . 43 1 . 53 ± 0 . 41 2 . 58 ± 0 . 54 1 . 73 ± 0 . 53 1 . 69 ± 0 . 58 2 . 07 ± 0 . 55 1 . 19 ± 0 . 42 2 . 35 ± 0 . 53 0 . 75 ± 0 . 37 < 37 . 84 2 . 51 ± 1 . 66 1 . 68 ± 0 . 84 1 . 32 ± 0 . 61  \n(4 . 48 ± 0 . 49) × 10 -8 (3 . 45 ± 0 . 43) × 10 -8 (2 . 66 ± 0 . 37) × 10 -8 (2 . 06 ± 0 . 31) × 10 -8 (1 . 59 ± 0 . 27) × 10 -8 (1 . 23 ± 0 . 22) × 10 -8 < 2 . 17 × 10 -11 (6 . 61 ± 2 . 89) × 10 -12 (1 . 70 ± 0 . 88) × 10 -12 (6 . 91 ± 4 . 01) × 10 -13 < 4 . 32 × 10 -11 < 2 . 42 × 10 -11 (6 . 40 ± 1 . 65) × 10 -15 (4 . 08 ± 0 . 85) × 10 -15 (2 . 09 ± 0 . 50) × 10 -15 (1 . 75 ± 0 . 38) × 10 -15 (9 . 30 ± 2 . 49) × 10 -16 (10 . 56 ± 2 . 21) × 10 -16 (4 . 83 ± 1 . 48) × 10 -16 (3 . 22 ± 1 . 10) × 10 -16 (4 . 75 ± 1 . 26) × 10 -15 (1 . 70 ± 0 . 60) × 10 -15 (2 . 07 ± 0 . 4) × 10 -15 (4 . 19 ± 2 . 07) × 10 -16 < 3 . 47 × 10 -13 (9 . 03 ± 5 . 96) × 10 -15 (2 . 39 ± 1 . 20) × 10 -15 (7 . 58 ± 3 . 50) × 10 -16  \n2018-04-22-24 / 58230-58232 2018-04-22-24 / 58230-58232 2018-04-22-24 / 58230-58232 2018-04-22-24 / 58230-58232 2018-04-22-24 / 58230-58232 2018-04-22-24 / 58230-58232 2018-04-08-05-08 / 58216-58246 2018-04-08-05-08 / 58216-58246 2018-04-08-05-08 / 58216-58246 2018-04-08-05-08 / 58216-58246 2018-04-08-05-08 / 58216-58246 2018-04-08-05-08 / 58216-58246 2018-04-18-24 / 58226-58232 2018-04-18-24 / 58226-58232 2018-04-18-24 / 58226-58232 2018-04-18-24 / 58226-58232 2018-04-18-24 / 58226-58232 2018-04-18-24 / 58226-58232 2018-04-18-24 / 58226-58232 2018-04-18-24 / 58226-58232 2018-04-10-21 / 58218-58229 2018-04-10-21 / 58218-58229 2018-04-10-21 / 58218-58229 2018-04-10-21 / 58218-58229 2018-04-12-25 / 58220-58233 2018-04-12-25 / 58220-58233 2018-04-12-25 / 58220-58233 2018-04-12-25 / 58220-58233  \n0.8 0.8 0.8 0.8 0.8 0.8 ≃ 7000 ≃ 4500 ≃ 2800 ≃ 1100 ≃ 700 ≃ 350 ≃ 360 ≃ 360 ≃ 360 ≃ 190 ≃ 190 ≃ 190 ≃ 190 ≃ 190 ≃ 360 ≃ 360 ≃ 360 ≃ 360 GLYPH<27> 330 GLYPH<27> 300 GLYPH<27> 240 GLYPH<27> 190  \n3 . 43 × 10 18 4 . 39 × 10 18 5 . 62 × 10 18 7 . 18 × 10 18 9 . 19 × 10 18 1 . 18 × 10 19 4 . 31 × 10 22 1 . 18 × 10 23 2 . 79 × 10 23 6 . 63 × 10 24 1 . 57 × 10 24 7 . 65 × 10 24 9 . 35 × 10 25 1 . 37 × 10 26 2 . 00 × 10 26 2 . 93 × 10 26 4 . 30 × 10 26 6 . 29 × 10 26 9 . 21 × 10 26 1 . 36 × 10 27 9 . 62 × 10 25 1 . 53 × 10 26 2 . 42 × 10 26 3 . 83 × 10 26 1 . 21 × 10 25 3 . 04 × 10 25 7 . 64 × 10 25 1 . 92 × 10 26  \n12.6 -16.1 k eV 16.1 -20.5 k eV 20.5 -26.3 k eV 26.3 -33.6 k eV 33.6 -43.0 k eV 43.0 -55.0 k eV 0.1 -0.32 GeV 0.32 -0.75 GeV 0.75 -1.78 GeV 1.78 -4.22 GeV 4.22 -10 GeV 10 -100 GeV 0.32 -0.46 T eV 0.46 -0.68 T eV 0.68 -1.00 T eV 1.00 -1.47 T eV 1.47 -2.15 T eV 2.15 -3.16 T eV 3.16 -4.64 T eV 4.64 -6.81 T eV 0.32 -0.50 T eV 0.50 -0.79 T eV 0.79 -1.26 T eV 1.26 -2.00 T eV 0.03 -0.075 T eV 0.075 -0.19 T eV 0.19 -0.48 T eV 0.48 -1.2 T eV', 'AR AR AR AR AR AR': 'Chandr a + NuST Chandr a + NuST Chandr a + NuST Chandr a + NuST Chandr a + NuST Chandr a + NuST F ermi -LA T F ermi -LA T F ermi -LA T F ermi -LA T F ermi -LA T F ermi -LA T H.E.S.S. H.E.S.S. H.E.S.S. H.E.S.S. H.E.S.S. H.E.S.S. H.E.S.S. H.E.S.S. VERIT AS VERIT AS VERIT AS VERIT AS MA GIC MA GIC MA GIC MA GIC  \nNotes. ( a ) Spatial scale of the source emission (FWHM of the instrumental beam or PSF). Besides the EHT observ ation for other radio observ ations if the core is not resolv ed we use the flux peak obtained from the beam size as an upper limit on the core emission. F or elliptical beams, we list the a v erage of the ax es as a representati v e angular scale. At higher ener gies, the scale typically corresponds to the PSF within a gi v en band or the diameter of the re gion used for data e xtraction.  \nTable D.1. Fractional variability of the VHE γ -ray and X-ray bands during the observational campaign in March 2018. For comparison, we also show F var for the same bands in 2017. The calculation of F var of the total flux in 2018 could not be performed because the calculated sample variance was smaller than the measurement uncertainties.  \n.  \n.  \n.  \n.', 'Appendix D: Multi-wavelength light curve variability': 'We estimate the degree of variability in di ff erent wavebands by calculating the fractional variability F var from Eq. 10 in Vaughan et al. (2003):  \nF var = s S 2 -σ 2 err x 2 (D.1)  \nwhere S 2 = 1 N -1 P N i = 1 ( xi -x ) 2 is the sample variance where x is the arithmetic mean of the flux measurements xi and σ 2 err = 1 N P N i = 1 σ 2 err , i the mean squared measurement uncertainty. The uncertainties of F var are calculated following Poutanen et al. (2008) as:  \n∆ F var = q F 2 var + err GLYPH<16> σ 2 NXS GLYPH<17> -F var (D.2)  \nwith  \nerr GLYPH<16> σ 2 NXS GLYPH<17> = v u u u u u t        r 2 N σ 2 err x 2        2 +            s σ 2 err N 2 F var x 2            2 . (D.3)  \nWepresent the calculated values of F var for the 2018 observational campaign in Table D.1 and also show the same values of F var during the 2017 campaign (M87 MWL2017). As described in Sect. 2.4.2 the observed VHE flare has a time variability scale O (1 day). Therefore, following the Nyquist-Shannon sampling theorem (Whittaker 1915), flux points closer than 0.5 days are combined using their weighted average value. To be able to compare the fractional variabilities of di ff erent bands it is necessary to only select simultaneous data. Therefore, we can only compare the fractional variability of the VHE γ -ray instruments and Swift -XRT listed in Table D.1.'}