We present results from a search for Xraygammaray counterparts of gravitationalwave GW candidates from the third observing run O3 of the LIGOVirgoKAGRA LVK network using the Swift Burst Alert Telescope SwiftBAT. The search includes 636 GW candidates received in low latency 86 of which have been confirmed by the offline analysis and included in the third cumulative GravitationalWave Transient Catalogs GWTC3. Targeted searches were carried out on the entire GW sample using the maximumlikelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the SwiftBAT false alarm rate is less than 103 Hz we compute the GWBAT joint false alarm rate. Finally the derived SwiftBAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers.
['Astrophysics - High Energy Astrophysical Phenomena', 'General Relativity and Quantum Cosmology']
SwiftBAT GUANO followup of gravitationalwave triggers in the third LIGOVirgoKAGRA observing run
{'The Swift-BAT/GUANO Team': "Gayathri Raman, 1 Samuele Ronchini, 1 James Delaunay, 2, 1 Aaron Tohuvavohu, 3, 4 Jamie A. Kennea, 1 Tyler Parsotan, 5  \nThe Swift Collaboration  \nElena Ambrosi, 6 Maria Grazia Bernardini, 7 Sergio Campana, 7 Giancarlo Cusumano, 6 Antonino D'A'ı, 6 Paolo D'Avanzo, 7 Valerio D'Elia, 8, 9 Massimiliano De Pasquale, 10 Simone Dichiara, 1 Phil Evans, 11 Dieter Hartmann, 12 Paul Kuin, 13 Andrea Melandri, 14 Paul O'Brien, 11 Julian P. Osborne, 11 Kim Page, 11 David M. Palmer, 15 Boris Sbarufatti, 16 Gianpiero Tagliaferri, 7 Eleonora Troja, 17  \nThe LIGO Scientific Collaboration, the Virgo Collaboration, and the KAGRA Collaboration  \nA. G. Abac, 18 R. Abbott, 19 H. Abe, 20 I. Abouelfettouh, 21 F. Acernese, 22, 23 K. Ackley, 24 C. Adamcewicz, 25 S. Adhicary, 26 N. Adhikari, 27 R. X. Adhikari, 19 V. K. Adkins, 28 V. B. Adya, 29 C. Affeldt, 30, 31 D. Agarwal, 32 M. Agathos, 33 O. D. Aguiar, 34 I. Aguilar, 35 L. Aiello, 36 A. Ain, 37 T. Akutsu, 38, 39 S. Albanesi, 40, 41 R. A. Alfaidi, 42 A. Al-Jodah, 43 C. All'en'e, 44 A. Allocca, 45, 23 S. Al-Shammari, 36 P. A. Altin, 29 S. Alvarez-Lopez, 46 A. Amato, 47, 48 L. Amez-Droz, 49 A. Amorosi, 49 C. Amra, 50 S. Anand, 19 A. Ananyeva, 19 S. B. Anderson, 19 W. G. Anderson, 19 M. Andia, 51 M. Ando, 52 T. Andrade, 53 N. Andres, 44 M. Andr'es-Carcasona, 54 T. Andri'c, 18, 55 J. Anglin, 56 S. Ansoldi, 57, 58 J. M. Antelis, 59 S. Antier, 60 M. Aoumi, 61 E. Z. Appavuravther, 62, 63 S. Appert, 19 S. K. Apple, 64 K. Arai, 19 A. Araya, 65 M. C. Araya, 19 J. S. Areeda, 66 N. Aritomi, 21 F. Armato, 67 N. Arnaud, 51, 68 M. Arogeti, 69 S. M. Aronson, 28 G. Ashton, 70 Y. Aso, 38, 71 M. Assiduo, 72, 73 S. Assis de Souza Melo, 68 S. M. Aston, 74 P. Astone, 75 F. Aubin, 76 K. AultONeal, 59 G. Avallone, 77 S. Babak, 78 F. Badaracco, 67 C. Badger, 79 S. Bae, 80 S. Bagnasco, 41 E. Bagui, 81 Y. Bai, 19 J. G. Baier, 82 R. Bajpai, 38 T. Baka, 83 M. Ball, 84 G. Ballardin, 68 S. W. Ballmer, 85 S. Banagiri, 86 B. Banerjee, 55 D. Bankar, 32 P. Baral, 27 J. C. Barayoga, 19 B. C. Barish, 19 D. Barker, 21 P. Barneo, 53, 87 F. Barone, 88, 23 B. Barr, 42 L. Barsotti, 46 M. Barsuglia, 78 D. Barta, 89 S. D. Barthelmy, 90 M. A. Barton, 42 I. Bartos, 56 S. Basak, 91 A. Basalaev, 92 R. Bassiri, 35 A. Basti, 93, 37 M. Bawaj, 94, 62 P. Baxi, 95 J. C. Bayley, 42 A. C. Baylor, 27 M. Bazzan, 96, 97 B. B'ecsy, 98 V. M. Bedakihale, 99 F. Beirnaert, 100 M. Bejger, 101 D. Belardinelli, 102 A. S. Bell, 42 V. Benedetto, 103 D. Beniwal, 104 W. Benoit, 105 J. D. Bentley, 92 M. Ben Yaala, 106 S. Bera, 107 M. Berbel, 108 F. Bergamin, 30, 31 B. K. Berger, 35 S. Bernuzzi, 109 M. Beroiz, 19 C. P. L. Berry, 42 D. Bersanetti, 67 A. Bertolini, 48 J. Betzwieser, 74 D. Beveridge, 43 N. Bevins, 110 R. Bhandare, 111 U. Bhardwaj, 112, 48 R. Bhatt, 19 D. Bhattacharjee, 82, 113 S. Bhaumik, 56 S. Bhowmick, 114 A. Bianchi, 48, 115 I. A. Bilenko, 116 G. Billingsley, 19 A. Binetti, 117 S. Bini, 118, 119 O. Birnholtz, 120 S. Biscoveanu, 86, 46 A. Bisht, 31 M. Bitossi, 68, 37 M.-A. Bizouard, 60 J. K. Blackburn, 19 C. D. Blair, 43, 74 D. G. Blair, 43 F. Bobba, 77, 121 N. Bode, 30, 31 G. Bogaert, 60 G. Boileau, 122, 60 M. Boldrini, 123, 75 G. N. Bolingbroke, 104 A. Bolliand, 124, 50 L. D. Bonavena, 96 R. Bondarescu, 53 F. Bondu, 125 E. Bonilla, 35 M. S. Bonilla, 66 A. Bonino, 126 R. Bonnand, 44 P. Booker, 30, 31 A. Borchers, 30, 31 V. Boschi, 37 S. Bose, 32 V. Bossilkov, 74 V. Boudart, 127 A. Boumerdassi, 36 A. Bozzi, 68 C. Bradaschia, 37 P. R. Brady, 27 M. Braglia, 128 A. Branch, 74 M. Branchesi, 55, 129 M. Breschi, 109 T. Briant, 130 A. Brillet, 60 M. Brinkmann, 30, 31 P. Brockill, 27 E. Brockmueller, 30, 31 A. F. Brooks, 19 D. D. Brown, 104 M. L. Brozzetti, 94, 62 S. Brunett, 19 G. Bruno, 131 R. Bruntz, 132 J. Bryant, 126 F. Bucci, 73 J. Buchanan, 132 O. Bulashenko, 53, 87 T. Bulik, 133 H. J. Bulten, 48 A. Buonanno, 134, 18 K. Burtnyk, 21 R. Buscicchio, 135, 136 D. Buskulic, 44 C. Buy, 137 R. L. Byer, 35 G. S. Cabourn Davies, 138 G. Cabras, 57, 58 R. Cabrita, 131 L. Cadonati, 69 G. Cagnoli, 139 C. Cahillane, 85 J. Calder'on Bustillo, 140 J. D. Callaghan, 42 T. A. Callister, 141 E. Calloni, 45, 23 J. B. Camp, 90 M. Canepa, 142, 67 G. Caneva Santoro, 54 M. Cannavacciuolo, 77 K. C. Cannon, 52 H. Cao, 143 Z. Cao, 144 L. A. Capistran, 145 E. Capocasa, 78 E. Capote, 85 G. Carapella, 77, 121 F. Carbognani, 68 M. Carlassara, 30, 31 J. B. Carlin, 146 M. Carpinelli, 135, 147, 68 G. Carrillo, 84 J. J. Carter, 30, 31 G. Carullo, 148 J. Casanueva Diaz, 68 C. Casentini, 149, 102 G. Castaldi, 150 S. Y. Castro-Lucas, 114 S. Caudill, 151, 48, 83 M. Cavagli'a, 113 R. Cavalieri, 68 G. Cella, 37 P. Cerd'a-Dur'an, 152, 153 E. Cesarini, 102 W. Chaibi, 60 P. Chakraborty, 30, 31 S. Chalathadka Subrahmanya, 92 C. Chan, 52 J. C. L. Chan, 141 K. H. M. Chan, 154 M. Chan, 155 W. L. Chan, 154 K. Chandra, 156 R.-J. Chang, 157 P. Chanial, 78 S. Chao, 158, 159 C. Chapman-Bird, 42 E. L. Charlton, 132 P. Charlton, 160 E. Chassande-Mottin, 78 C. Chatterjee, 43 Debarati Chatterjee, 32 Deep Chatterjee, 46 M. Chaturvedi, 111 S. Chaty, 78 A. Chen, 161 A. H.-Y. Chen, 162 D. Chen, 163 H. Chen, 158 H. Y. Chen, 164 K. H. Chen, 159 X. Chen, 43 Yi-Ru Chen, 158 Yanbei Chen, 165 Yitian Chen, 166 H. P. Cheng, 56 P. Chessa, 93, 37 H. T. Cheung, 95 H. Y. Chia, 56 F. Chiadini, 167, 121 C. Chiang, 159 G. Chiarini, 97 A. Chiba, 168 R. Chiba, 169 R. Chierici, 170 A. Chincarini, 67 M. L. Chiofalo, 93, 37 A. Chiummo, 23, 68 C. Chou, 162 S. Choudhary, 43 N. Christensen, 60 S. S. Y. Chua, 29 K. W. Chung, 79 G. Ciani, 96, 97 P. Ciecielag, 101 M. Cie'slar, 101 M. Cifaldi, 102 A. A. Ciobanu, 104  \nR. Ciolfi, 171, 97 F. Clara, 21 J. A. Clark, 19, 69 T. A. Clarke, 25 P. Clearwater, 172 S. Clesse, 81 F. Cleva, 60 E. Coccia, 55, 129, 54 E. Codazzo, 55 P.-F. Cohadon, 130 M. Colleoni, 107 C. G. Collette, 49 J. Collins, 74 S. Colloms, 42 A. Colombo, 135, 136, 173 M. Colpi, 135, 136 C. M. Compton, 21 L. Conti, 97 S. J. Cooper, 126 T. R. Corbitt, 28 I. Cordero-Carri'on, 174 S. Corezzi, 94, 62 N. J. Cornish, 98 A. Corsi, 175 S. Cortese, 68 C. A. Costa, 34 R. Cottingham, 74 M. W. Coughlin, 105 A. Couineaux, 75 J.-P. Coulon, 60 S. T. Countryman, 176 J.-F. Coupechoux, 170 B. Cousins, 26 P. Couvares, 19, 69 D. M. Coward, 43 M. J. Cowart, 74 D. C. Coyne, 19 R. Coyne, 177 K. Craig, 106 R. Creed, 36 J. D. E. Creighton, 27 T. D. Creighton, 178 P. Cremonese, 107 A. W. Criswell, 105 J. C. G. Crockett-Gray, 28 M. Croquette, 130 R. Crouch, 21 S. G. Crowder, 179 J. R. Cudell, 127 T. J. Cullen, 19 A. Cumming, 42 E. Cuoco, 68, 180, 37 M. Cusinato, 152 P. Dabadie, 139 T. Dal Canton, 51 S. Dall'Osso, 75 G. D'alya, 100 B. D'Angelo, 67 S. Danilishin, 47, 48 S. D'Antonio, 102 K. Danzmann, 31, 30, 31 K. E. Darroch, 132 L. P. Dartez, 21 A. Dasgupta, 99 S. Datta, 181 V. Dattilo, 68 A. Daumas, 78 N. Davari, 182, 147 I. Dave, 111 A. Davenport, 114 M. Davier, 51 T. F. Davies, 43 D. Davis, 19 L. Davis, 43 M. C. Davis, 110 E. J. Daw, 183 M. Dax, 18 J. De Bolle, 100 M. Deenadayalan, 32 J. Degallaix, 184 M. De Laurentis, 45, 23 S. Del'eglise, 130 V. Del Favero, 90 F. De Lillo, 131 D. Dell'Aquila, 182, 147 W. Del Pozzo, 93, 37 F. De Marco, 75, 123 F. De Matteis, 149, 102 V. D'Emilio, 36 N. Demos, 46 T. Dent, 140 A. Depasse, 131 N. DePergola, 110 R. De Pietri, 185, 186 R. De Rosa, 45, 23 C. De Rossi, 68 R. De Simone, 167 A. Dhani, 18 S. Dhurandhar, 32 R. Diab, 56 M. C. D'ıaz, 178 M. Di Cesare, 45 G. Dideron, 187 N. A. Didio, 85 T. Dietrich, 18 L. Di Fiore, 23 C. Di Fronzo, 49 F. Di Giovanni, 152 M. Di Giovanni, 123, 75 T. Di Girolamo, 45, 23 D. Diksha, 48, 47 A. Di Michele, 94 J. Ding, 78, 188 S. Di Pace, 123, 75 I. Di Palma, 123, 75 F. Di Renzo, 170 Divyajyoti, 189 A. Dmitriev, 126 Z. Doctor, 86 E. Dohmen, 21 P. P. Doleva, 132 L. Donahue, 190 L. D'Onofrio, 75 F. Donovan, 46 K. L. Dooley, 36 T. Dooney, 83 S. Doravari, 32 O. Dorosh, 191 M. Drago, 123, 75 J. C. Driggers, 21 Y. Drori, 19 J.-G. Ducoin, 192, 78 L. Dunn, 146 U. Dupletsa, 55 D. D'Urso, 182, 147 H. Duval, 193 P.-A. Duverne, 51 S. E. Dwyer, 21 C. Eassa, 21 M. Ebersold, 194, 44 T. Eckhardt, 92 G. Eddolls, 42 B. Edelman, 84 T. B. Edo, 19 O. Edy, 138 A. Effler, 74 J. Eichholz, 29 H. Einsle, 60 M. Eisenmann, 38 R. A. Eisenstein, 46 A. Ejlli, 36 M. Emma, 70 E. Engelby, 66 A. J. Engl, 35 L. Errico, 45, 23 R. C. Essick, 195 H. Estell'es, 18 D. Estevez, 76 T. Etzel, 19 M. Evans, 46 T. Evstafyeva, 33 B. E. Ewing, 26 J. M. Ezquiaga, 141 F. Fabrizi, 72, 73 F. Faedi, 73, 72 V. Fafone, 149, 102 S. Fairhurst, 36 P. C. Fan, 190 A. M. Farah, 141 B. Farr, 84 W. M. Farr, 196, 197 G. Favaro, 96 M. Favata, 198 M. Fays, 127 M. Fazio, 106 J. Feicht, 19 M. M. Fejer, 35 E. Fenyvesi, 89, 199 D. L. Ferguson, 164 I. Ferrante, 93, 37 T. A. Ferreira, 28 F. Fidecaro, 93, 37 A. Fiori, 37, 93 I. Fiori, 68 M. Fishbach, 195 R. P. Fisher, 132 R. Fittipaldi, 200, 121 V. Fiumara, 201, 121 R. Flaminio, 44 S. M. Fleischer, 202 L. S. Fleming, 203 E. Floden, 105 E. M. Foley, 105 H. Fong, 155 J. A. Font, 152, 153 B. Fornal, 204 P. W. F. Forsyth, 29 K. Franceschetti, 185 N. Franchini, 78 S. Frasca, 123, 75 F. Frasconi, 37 A. Frattale Mascioli, 123, 75 Z. Frei, 205 A. Freise, 48, 115 O. Freitas, 206, 152 R. Frey, 84 W. Frischhertz, 74 P. Fritschel, 46 V. V. Frolov, 74 G. G. Fronz'e, 41 M. Fuentes-Garcia, 19 S. Fujii, 169 I. Fukunaga, 207 P. Fulda, 56 M. Fyffe, 74 W. E. Gabella, 208 B. Gadre, 83 J. R. Gair, 18 S. Galaudage, 25, 209 S. Gallardo, 210 B. Gallego, 210 R. Gamba, 109 A. Gamboa, 18 D. Ganapathy, 46 A. Ganguly, 32 S. G. Gaonkar, 32 B. Garaventa, 67, 142 J. Garcia-Bellido, 128 C. Garc'ıa-N'u˜nez, 203 C. Garc'ıa-Quir'os, 194 J. W. Gardner, 29 K. A. Gardner, 155 J. Gargiulo, 68 A. Garron, 107 F. Garufi, 45, 23 C. Gasbarra, 149, 102 B. Gateley, 21 V. Gayathri, 27 G. Gemme, 67 A. Gennai, 37 J. George, 111 R. George, 164 O. Gerberding, 92 L. Gergely, 211 N. Ghadiri, 66 Archisman Ghosh, 100 Shaon Ghosh, 198 Shrobana Ghosh, 30, 31 Suprovo Ghosh, 32 Tathagata Ghosh, 32 L. Giacoppo, 123, 75 J. A. Giaime, 28, 74 K. D. Giardina, 74 D. R. Gibson, 203 D. T. Gibson, 33 C. Gier, 106 P. Giri, 37, 93 F. Gissi, 103 S. Gkaitatzis, 93, 37 J. Glanzer, 28 A. E. Gleckl, 66 F. Glotin, 51 J. Godfrey, 84 P. Godwin, 19 N. L. Goebbels, 92 E. Goetz, 155 J. Golomb, 19 S. Gomez Lopez, 123, 75 B. Goncharov, 55 G. Gonz'alez, 28 P. Goodarzi, 143 A. W. Goodwin-Jones, 43 M. Gosselin, 68 A. S. Gottel, 36 R. Gouaty, 44 D. W. Gould, 29 S. Goyal, 91 B. Grace, 29 A. Grado, 212, 23 V. Graham, 42 A. E. Granados, 105 M. Granata, 184 V. Granata, 77 L. Granda Argianas, 110 S. Gras, 46 P. Grassia, 19 C. Gray, 21 R. Gray, 42 G. Greco, 62 A. C. Green, 48, 115 S. M. Green, 138 S. R. Green, 18 A. M. Gretarsson, 59 E. M. Gretarsson, 59 D. Griffith, 19 W. L. Griffiths, 36 H. L. Griggs, 69 G. Grignani, 94, 62 A. Grimaldi, 118, 119 C. Grimaud, 44 H. Grote, 36 A. S. Gruson, 66 D. Guerra, 152 D. Guetta, 213, 75 G. M. Guidi, 72, 73 A. R. Guimaraes, 28 H. K. Gulati, 99 F. Gulminelli, 214, 215 A. M. Gunny, 46 H. Guo, 204 W. Guo, 43 Y. Guo, 48, 47 Anchal Gupta, 19 Anuradha Gupta, 216 Ish Gupta, 26 N. C. Gupta, 99 P. Gupta, 48, 83 S. K. Gupta, 56 T. Gupta, 98 N. Gupte, 18 R. Gurav, 143 J. Gurs, 92 N. Gutierrez, 184 F. Guzman, 145 D. Haba, 20 M. Haberland, 18 L. Haegel, 78 G. Hain, 132 S. Haino, 217 E. D. Hall, 46 E. Z. Hamilton, 194 G. Hammond, 42 W.-B. Han, 218 M. Haney, 194, 48 J. Hanks, 21 C. Hanna, 26 M. D. Hannam, 36 O. A. Hannuksela, 154 A. G. Hanselman, 141 H. Hansen, 21 J. Hanson, 74 R. Harada, 52 T. Harder, 60 K. Haris, 48, 83 T. Harmark, 148 J. Harms, 55, 129 G. M. Harry, 219 I. W. Harry, 138 B. Haskell, 101 C.-J. Haster, 220 J. S. Hathaway, 221 K. Haughian, 42 H. Hayakawa, 61 K. Hayama, 222 J. Healy, 221 A. Heffernan, 107 A. Heidmann, 130 M. C. Heintze, 74 J. Heinze, 126 J. Heinzel, 46 H. Heitmann, 60 F. Hellman, 223 P. Hello, 51 A. F. Helmling-Cornell, 84 G. Hemming, 68 M. Hendry, 42 I. S. Heng, 42 E. Hennes, 48 J.-S. Hennig, 47, 48 M. Hennig, 47, 48 C. Henshaw, 69 A. Hernandez, 198 T. Hertog, 117 M. Heurs, 30, 31 A. L. Hewitt, 33, 224 S. Higginbotham, 36 S. Hild, 47, 48 P. Hill, 106 S. Hill, 42 Y. Himemoto, 225 A. S. Hines, 145 N. Hirata, 38 C. Hirose, 226 J. Ho, 159 S. Hoang, 51 S. Hochheim, 30, 31 D. Hofman, 184 N. A. Holland, 48, 115 K. Holley-Bockelmann, 208 I. J. Hollows, 183 Z. J. Holmes, 104 D. E. Holz, 141 C. Hong, 35 J. Hornung, 84 S. Hoshino, 226 J. Hough, 42 S. Hourihane, 19 E. J. Howell, 43 C. G. Hoy, 138 D. Hoyland, 126 C. A. Hrishikesh, 149 H.-F. Hsieh, 158 C. Hsiung, 227 H. C. Hsu, 159 S.-C. Hsu, 64, 158 W.-F. Hsu, 117 P. Hu, 208 Q. Hu, 42 H. Y. Huang, 159 Y.-J. Huang, 26 Y. Huang, 46 Y. T. Huang, 64 A. D. Huddart, 228 B. Hughey, 59 D. C. Y. Hui, 229 V. Hui, 44 R. Hur, 84 S. Husa, 107 R. Huxford, 26 T. Huynh-Dinh, 74 A. Iakovlev, 230 G. A. Iandolo, 47 A. Iess, 180, 37  \nK. Inayoshi, 231 Y. Inoue, 159 G. Iorio, 96 J. Irwin, 42 M. Isi, 196, 197 M. A. Ismail, 159 Y. Itoh, 207, 232 M. Iwaya, 169 B. R. Iyer, 91 V. JaberianHamedan, 43 P.-E. Jacquet, 130 S. J. Jadhav, 233 S. P. Jadhav, 172 T. Jain, 33 A. L. James, 36 P. A. James, 132 R. Jamshidi, 49 A. Z. Jan, 164 K. Jani, 208 L. Janiurek, 42 J. Janquart, 83, 48 K. Janssens, 122, 60 N. N. Janthalur, 233 S. Jaraba, 128 P. Jaranowski, 234 P. Jasal, 53 R. Jaume, 107 W. Javed, 36 A. Jennings, 21 W. Jia, 46 J. Jiang, 56 H.-B. Jin, 235, 236 K. Johansmeyer, 198 G. R. Johns, 132 N. A. Johnson, 56 R. Johnston, 42 N. Johny, 30, 31 D. H. Jones, 29 D. I. Jones, 237 R. Jones, 42 S. Jose, 189 P. Joshi, 26 L. Ju, 43 K. Jung, 238 J. Junker, 30, 31 V. Juste, 76 T. Kajita, 239 C. Kalaghatgi, 83, 48, 240 V. Kalogera, 86 M. Kamiizumi, 61 N. Kanda, 232, 207 S. Kandhasamy, 32 G. Kang, 241 J. B. Kanner, 19 S. J. Kapadia, 32 D. P. Kapasi, 29 S. Karat, 19 C. Karathanasis, 54 S. Karki, 113 R. Kashyap, 26 M. Kasprzack, 19 W. Kastaun, 30, 31 J. Kato, 168 T. Kato, 169 S. Katsanevas, 68, ∗ E. Katsavounidis, 46 W. Katzman, 74 T. Kaur, 43 R. Kaushik, 111 K. Kawabe, 21 D. Keitel, 107 J. Kelley-Derzon, 56 J. Kennington, 26 R. Kesharwani, 32 J. S. Key, 242 S. Khadka, 35 F. Y. Khalili, 116 F. Khan, 30, 31 I. Khan, 243, 50 T. Khanam, 175 E. A. Khazanov, 230 M. Khursheed, 111 W. Kiendrebeogo, 60, 244 N. Kijbunchoo, 104 C. Kim, 245 J. C. Kim, 246 K. Kim, 247 M. H. Kim, 248 S. Kim, 229 W. S. Kim, 249 Y.-M. Kim, 247 C. Kimball, 86 N. Kimura, 61 M. Kinley-Hanlon, 42 M. Kinnear, 36 J. S. Kissel, 21 T. Kiyota, 207 S. Klimenko, 56 T. Klinger, 36 A. M. Knee, 155 N. Knust, 30, 31 P. Koch, 30, 31 S. M. Koehlenbeck, 35 G. Koekoek, 48, 47 K. Kohri, 250 K. Kokeyama, 36 S. Koley, 55 P. Kolitsidou, 126 M. Kolstein, 54 K. Komori, 52 A. K. H. Kong, 158 A. Kontos, 251 M. Korobko, 92 R. V. Kossak, 30, 31 X. Kou, 105 A. Koushik, 122 N. Kouvatsos, 79 M. Kovalam, 43 N. Koyama, 226 D. B. Kozak, 19 S. L. Kranzhoff, 47, 48 V. Kringel, 30, 31 N. V. Krishnendu, 91 A. Kr'olak, 252, 191 G. Kuehn, 30, 31 P. Kuijer, 48 S. Kulkarni, 216 A. Kulur Ramamohan, 29 A. Kumar, 233 Praveen Kumar, 140 Prayush Kumar, 91 Rahul Kumar, 21 Rakesh Kumar, 99 J. Kume, 52 K. Kuns, 46 S. Kuroyanagi, 128, 253 S. Kuwahara, 52 K. Kwak, 238 K. Kwan, 29 G. Lacaille, 42 P. Lagabbe, 44 D. Laghi, 137 S. Lai, 162 A. H. Laity, 177 M. H. Lakkis, 49 E. Lalande, 254 M. Lalleman, 122 M. Landry, 21 B. B. Lane, 46 R. N. Lang, 46 J. Lange, 164 B. Lantz, 35 A. La Rana, 75 I. La Rosa, 107, 123, 44 A. Lartaux-Vollard, 51 P. D. Lasky, 25 J. Lawrence, 175 M. Laxen, 74 A. Lazzarini, 19 C. Lazzaro, 96, 97 P. Leaci, 123, 75 S. LeBohec, 204 Y. K. Lecoeuche, 155 H. M. Lee, 246 H. W. Lee, 255 K. Lee, 248 R.-K. Lee, 158 R. Lee, 46 S. Lee, 247 Y. Lee, 159 I. N. Legred, 19 J. Lehmann, 30, 31 L. Lehner, 187 A. Lemaˆıtre, 256 M. Lenti, 73, 257 M. Leonardi, 258, 38 E. Leonova, 112 M. Lequime, 50 N. Leroy, 51 M. Lesovsky, 19 N. Letendre, 44 M. Lethuillier, 170 C. Levesque, 254 Y. Levin, 25 K. Leyde, 78 A. K. Y. Li, 19 K. L. Li, 157 T. G. F. Li, 154, 117 X. Li, 165 Chien-Yu Lin, 159, 158 Chun-Yu Lin, 259 E. T. Lin, 158 F. Lin, 159 H. Lin, 159 L. C.-C. Lin, 157 F. Linde, 240, 48 S. D. Linker, 150, 210 T. B. Littenberg, 260 A. Liu, 154 G. C. Liu, 227 Jian Liu, 43 F. Llamas, 178 J. Llobera-Querol, 107 R. K. L. Lo, 19 J.-P. Locquet, 117 L. London, 112 A. Longo, 72, 73 D. Lopez, 194 M. Lopez Portilla, 83 M. Lorenzini, 149, 102 V. Loriette, 51 M. Lormand, 74 G. Losurdo, 37 T. P. Lott IV, 69 J. D. Lough, 30, 31 H. A. Loughlin, 46 C. O. Lousto, 221 M. J. Lowry, 132 H. Luck, 31, 30, 31 D. Lumaca, 102 A. P. Lundgren, 138 A. W. Lussier, 254 L.-T. Ma, 158 S. Ma, 165 M. Ma'arif, 159 R. Macas, 138 M. MacInnis, 46 R. R. Maciy, 30, 31 D. M. Macleod, 36 I. A. O. MacMillan, 19 A. Macquet, 54 D. Macri, 46 K. Maeda, 168 S. Maenaut, 117 I. Maga˜na Hernandez, 27 S. S. Magare, 32 C. Magazz'u, 37 R. M. Magee, 19 E. Maggio, 18 R. Maggiore, 48, 115 M. Magnozzi, 67, 142 M. Mahesh, 92 S. Mahesh, 261 M. Maini, 177 S. Majhi, 32 E. Majorana, 123, 75 C. N. Makarem, 19 J. A. Malaquias-Reis, 34 S. Maliakal, 19 A. Malik, 111 N. Man, 60 V. Mandic, 105 V. Mangano, 75, 123 B. Mannix, 84 G. L. Mansell, 85, 46 M. Manske, 27 M. Mantovani, 68 M. Mapelli, 96, 97 F. Marchesoni, 63, 62, 262 D. Mar'ın Pina, 53, 87, 263 F. Marion, 44 S. M'arka, 176 Z. M'arka, 176 C. Markakis, 161 A. S. Markosyan, 35 A. Markowitz, 19 E. Maros, 19 A. Marquina, 174 S. Marsat, 137 F. Martelli, 72, 73 I. W. Martin, 42 R. M. Martin, 198 B. B. Martinez, 145 M. Martinez, 54, 264 V. Martinez, 139 A. Martini, 118 K. Martinovic, 79 J. C. Martins, 34 D. V. Martynov, 126 E. J. Marx, 46 L. Massaro, 47, 48 A. Masserot, 44 M. Masso-Reid, 42 M. Mastrodicasa, 75 S. Mastrogiovanni, 75 M. Mateu-Lucena, 107 M. Matiushechkina, 30, 31 M. Matsuyama, 207 N. Mavalvala, 46 N. Maxwell, 21 G. McCarrol, 74 R. McCarthy, 21 D. E. McClelland, 29 S. McCormick, 74 L. McCuller, 19 G. I. McGhee, 42 K. B. M. McGowan, 208 M. Mchedlidze, 198 C. McIsaac, 138 J. McIver, 155 K. McKinney, 179 A. McLeod, 43 T. McRae, 29 S. T. McWilliams, 261 D. Meacher, 27 A. K. Mehta, 18 Q. Meijer, 83 A. Melatos, 146 S. Mellaerts, 117 A. Menendez-Vazquez, 54 C. S. Menoni, 114 R. A. Mercer, 27 L. Mereni, 184 K. Merfeld, 84 E. L. Merilh, 74 J. R. M'erou, 107 J. D. Merritt, 84 M. Merzougui, 60 C. Messenger, 42 C. Messick, 27 M. Meyer-Conde, 207 F. Meylahn, 30, 31 A. Mhaske, 32 A. Miani, 118, 119 H. Miao, 265 I. Michaloliakos, 56 C. Michel, 184 Y. Michimura, 19, 52 H. Middleton, 126 A. L. Miller, 48 S. Miller, 19 M. Millhouse, 69 E. Milotti, 266, 58 Y. Minenkov, 102 N. Mio, 267 Ll. M. Mir, 54 L. Mirasola, 268, 75 M. Miravet-Ten'es, 152 C.-A. Miritescu, 54 A. K. Mishra, 91 A. Mishra, 32 C. Mishra, 189 T. Mishra, 56 A. L. Mitchell, 48, 115 J. G. Mitchell, 59 S. Mitra, 32 V. P. Mitrofanov, 116 G. Mitselmakher, 56 R. Mittleman, 46 O. Miyakawa, 61 S. Miyamoto, 169 S. Miyoki, 61 G. Mo, 46 L. Mobilia, 72, 73 L. M. Modafferi, 107 S. R. P. Mohapatra, 19 S. R. Mohite, 27 M. Molina-Ruiz, 223 C. Mondal, 214 M. Mondin, 210 M. Montani, 72, 73 C. J. Moore, 126 M. Morales, 66 D. Moraru, 21 F. Morawski, 101 A. More, 32 S. More, 32 C. Moreno, 59 G. Moreno, 21 S. Morisaki, 52, 169 Y. Moriwaki, 168 G. Morras, 128 A. Moscatello, 96 P. Mourier, 107 B. Mours, 76 C. M. Mow-Lowry, 48, 115 S. Mozzon, 138 F. Muciaccia, 123, 75 D. Mukherjee, 260 Samanwaya Mukherjee, 32 Soma Mukherjee, 178 Subroto Mukherjee, 99 Suvodip Mukherjee, 269, 187, 112 N. Mukund, 46 A. Mullavey, 74 J. Munch, 104 C. L. Mungioli, 43 M. Munn, 21 W. R. Munn Oberg, 270 M. Murakoshi, 271 P. G. Murray, 42 S. Muusse, 29 S. L. Nadji, 30, 31 A. Nagar, 41, 272 N. Nagarajan, 42 K. N. Nagler, 59 K. Nakamura, 38 H. Nakano, 273 M. Nakano, 19 D. Nandi, 28 V. Napolano, 68 P. Narayan, 216 I. Nardecchia, 149, 102 H. Narola, 83 L. Naticchioni, 75 R. K. Nayak, 274 B. F. Neil, 43 J. Neilson, 103, 121 A. Nelson, 145 T. J. N. Nelson, 74 M. Nery, 30, 31 A. Neunzert, 21 S. Ng, 66 C. Nguyen, 78 P. Nguyen, 84 L. Nguyen Quynh, 275 S. A. Nichols, 28 A. B. Nielsen, 276 G. Nieradka, 101 A. Niko, 159 Y. Nishino, 38, 277  \nA. Nishizawa, 52 S. Nissanke, 112, 48 E. Nitoglia, 170 W. Niu, 26 F. Nocera, 68 M. Norman, 36 C. North, 36 J. Novak, 124, 278, 279, 280 J. F. Nu˜no Siles, 128 G. Nurbek, 178 L. K. Nuttall, 138 K. Obayashi, 271 J. Oberling, 21 J. O'Dell, 228 M. Oertel, 124, 278, 279, 281, 280 A. Offermans, 117 G. Oganesyan, 55, 129 J. J. Oh, 249 K. Oh, 229 S. H. Oh, 249 T. O'Hanlon, 74 M. Ohashi, 61 M. Ohkawa, 226 F. Ohme, 30, 31 H. Ohta, 52 A. S. Oliveira, 176 R. Oliveri, 124, 278, 279 V. Oloworaran, 43 B. O'Neal, 132 K. Oohara, 282, 283 B. O'Reilly, 74 N. D. Ormsby, 132 M. Orselli, 62, 94 R. O'Shaughnessy, 221 Y. Oshima, 284 S. Oshino, 61 S. Ossokine, 18 C. Osthelder, 19 D. J. Ottaway, 104 A. Ouzriat, 170 H. Overmier, 74 B. J. Owen, 175 A. E. Pace, 26 R. Pagano, 28 M. A. Page, 38 A. Pai, 156 S. A. Pai, 111 A. Pal, 285 S. Pal, 274 M. A. Palaia, 37, 93 O. Palashov, 230 M. P'alfi, 205 P. P. Palma, 149, 102 C. Palomba, 75 K. C. Pan, 158 P. K. Panda, 233 L. Panebianco, 72, 73 P. T. H. Pang, 48, 83 F. Pannarale, 123, 75 B. C. Pant, 111 F. H. Panther, 43 C. D. Panzer, 105 F. Paoletti, 37 A. Paoli, 68 A. Paolone, 75, 286 E. E. Papalexakis, 143 L. Papalini, 37, 93 G. Papigkiotis, 287 A. Parisi, 48, 112 J. Park, 247 W. Parker, 74 G. Pascale, 30, 31 D. Pascucci, 100 A. Pasqualetti, 68 R. Passaquieti, 93, 37 D. Passuello, 37 O. Patane, 21 M. Patel, 132 D. Pathak, 32 M. Pathak, 104 A. Patra, 36 B. Patricelli, 93, 37 A. S. Patron, 28 S. Paul, 84 E. Payne, 19 T. Pearce, 36 M. Pedraza, 19 R. Pegna, 37 A. Pele, 19 F. E. Pe˜na Arellano, 61 S. Penn, 270 M. D. Penuliar, 66 A. Perego, 118, 119 A. Pereira, 139 J. J. Perez, 56 C. P'erigois, 171, 97, 96 C. C. Perkins, 56 G. Perna, 96 A. Perreca, 118, 119 J. Perret, 78 S. Perri'es, 170 J. W. Perry, 48, 115 D. Pesios, 287 C. Petrillo, 94 H. P. Pfeiffer, 18 H. Pham, 74 K. A. Pham, 105 K. S. Phukon, 126, 48, 240 H. Phurailatpam, 154 O. J. Piccinni, 54 M. Pichot, 60 M. Piendibene, 93, 37 F. Piergiovanni, 72, 73 L. Pierini, 75 G. Pierra, 170 V. Pierro, 103, 121 M. Pietrzak, 101 M. Pillas, 51 F. Pilo, 37 L. Pinard, 184 C. Pineda-Bosque, 210 I. M. Pinto, 103, 121, 288, 45 M. Pinto, 68 B. J. Piotrzkowski, 27 M. Pirello, 21 M. D. Pitkin, 33, 224 A. Placidi, 62, 94 E. Placidi, 123, 75 M. L. Planas, 107 W. Plastino, 289, 290 R. Poggiani, 93, 37 E. Polini, 44 L. Pompili, 18 J. Poon, 154 E. Porcelli, 48 J. Portell, 53, 87, 263 E. K. Porter, 78 C. Posnansky, 26 R. Poulton, 68 J. Powell, 172 M. Pracchia, 44 B. K. Pradhan, 32 T. Pradier, 76 A. K. Prajapati, 99 K. Prasai, 35 R. Prasanna, 233 P. Prasia, 32 G. Pratten, 126 M. Principe, 150, 103, 288, 121 G. A. Prodi, 291, 119 L. Prokhorov, 126 P. Prosposito, 149, 102 L. Prudenzi, 18 A. Puecher, 48, 83 J. Pullin, 28 M. Punturo, 62 F. Puosi, 37, 93 P. Puppo, 75 M. Purrer, 177 H. Qi, 161 J. Qin, 29 G. Qu'em'ener, 215, 124, 214 V. Quetschke, 178 C. Quigley, 36 P. J. Quinonez, 59 R. Quitzow-James, 113 F. J. Raab, 21 G. Raaijmakers, 112, 48 N. Radulesco, 60 P. Raffai, 205 S. X. Rail, 254 S. Raja, 111 C. Rajan, 111 B. Rajbhandari, 221, 175 D. S. Ramirez, 59 K. E. Ramirez, 74 F. A. Ramis Vidal, 107 A. Ramos-Buades, 18 D. Rana, 32 E. Randel, 114 S. Ranjan, 69 P. Rapagnani, 123, 75 B. Ratto, 59 S. Rawat, 105 A. Ray, 27 V. Raymond, 36 M. Razzano, 93, 37 J. Read, 66 M. Recaman Payo, 117 T. Regimbau, 44 L. Rei, 67 S. Reid, 106 S. W. Reid, 132 D. H. Reitze, 19 P. Relton, 36 A. Renzini, 19 P. Rettegno, 41 B. Revenu, 78, 292 A. Reza, 48 M. Rezac, 66 A. S. Rezaei, 75, 123 F. Ricci, 123, 75 M. Ricci, 75 D. Richards, 228 C. J. Richardson, 59 J. W. Richardson, 143 A. Rijal, 59 K. Riles, 95 H. K. Riley, 36 S. Rinaldi, 93, 37 J. Rittmeyer, 92 C. Robertson, 228 F. Robinet, 51 M. Robinson, 21 A. Rocchi, 102 L. Rolland, 44 J. G. Rollins, 19 M. Romanelli, 125 A. E. Romano, 293 R. Romano, 22, 23 A. Romero, 193 I. M. Romero-Shaw, 33 J. H. Romie, 74 T. J. Roocke, 104 L. Rosa, 23, 45 T. J. Rosauer, 143 C. A. Rose, 27 D. Rosi'nska, 133 M. P. Ross, 64 M. Rossello, 107 S. Rowan, 42 S. K. Roy, 196, 197 S. Roy, 83 D. Rozza, 182, 147 P. Ruggi, 68 E. Ruiz Morales, 294, 128 K. Ruiz-Rocha, 208 S. Sachdev, 69 T. Sadecki, 21 J. Sadiq, 140 P. Saffarieh, 48, 115 M. R. Sah, 269 S. S. Saha, 158 T. Sainrat, 76 S. Sajith Menon, 213, 123, 75 K. Sakai, 295 M. Sakellariadou, 79 T. Sako, 168 S. Sakon, 26 O. S. Salafia, 173, 136, 135 F. Salces-Carcoba, 19 L. Salconi, 68 M. Saleem, 105 F. Salemi, 123, 75 M. Sall'e, 48 S. Salvador, 215, 214, 124 A. Sanchez, 21 E. J. Sanchez, 19 J. H. Sanchez, 86 L. E. Sanchez, 19 N. Sanchis-Gual, 296, 152 J. R. Sanders, 297 E. M. Sanger, 18 T. R. Saravanan, 32 N. Sarin, 25 A. Sasli, 287 P. Sassi, 62, 94 B. Sassolas, 184 H. Satari, 43 R. Sato, 226 S. Sato, 168 Y. Sato, 168 O. Sauter, 56 R. L. Savage, 21 T. Sawada, 61 H. L. Sawant, 32 S. Sayah, 44 D. Schaetzl, 19 M. Scheel, 165 J. Scheuer, 86 M. G. Schiworski, 104 P. Schmidt, 126 S. Schmidt, 83 R. Schnabel, 92 M. Schneewind, 30, 31 R. M. S. Schofield, 84 K. Schouteden, 117 H. Schuler, 26 B. W. Schulte, 30, 31 B. F. Schutz, 36, 30, 31 E. Schwartz, 36 J. Scott, 42 S. M. Scott, 29 T. C. Seetharamu, 42 M. Seglar-Arroyo, 54 Y. Sekiguchi, 298 D. Sellers, 74 A. S. Sengupta, 299 D. Sentenac, 68 E. G. Seo, 42 J. W. Seo, 117 V. Sequino, 45, 23 A. Sergeev, 230 M. Serra, 75 G. Servignat, 278 Y. Setyawati, 83 T. Shaffer, 21 U. S. Shah, 69 M. S. Shahriar, 86 M. A. Shaikh, 246 B. Shams, 204 L. Shao, 231 A. K. Sharma, 91 P. Sharma, 111 S. Sharma-Chaudhary, 113 P. Shawhan, 134 N. S. Shcheblanov, 300, 256 B. Shen, 134 Y. Shikano, 301, 302 M. Shikauchi, 52 K. Shimode, 61 H. Shinkai, 303 J. Shiota, 271 D. H. Shoemaker, 46 D. M. Shoemaker, 164 R. W. Short, 21 S. ShyamSundar, 111 A. Sider, 49 H. Siegel, 176, 196, 197 M. Sieniawska, 131 D. Sigg, 21 L. Silenzi, 62, 63 M. Simmonds, 104 L. P. Singer, 90 A. Singh, 216 D. Singh, 26 M. K. Singh, 91 A. Singha, 47, 48 A. M. Sintes, 107 V. Sipala, 182, 147 V. Skliris, 36 B. J. J. Slagmolen, 29 T. J. Slaven-Blair, 43 J. Smetana, 126 J. R. Smith, 66 L. Smith, 42 R. J. E. Smith, 25 W. J. Smith, 208 J. Soldateschi, 257, 304, 73 S. N. Somala, 305 K. Somiya, 20 K. Soni, 32 S. Soni, 46 V. Sordini, 170 F. Sorrentino, 67 N. Sorrentino, 93, 37 R. Soulard, 60 T. Souradeep, 32, 306 A. Southgate, 36 E. Sowell, 175 V. Spagnuolo, 47, 48 A. P. Spencer, 42 M. Spera, 96, 97 P. Spinicelli, 68 A. K. Srivastava, 99 F. Stachurski, 42 D. A. Steer, 78 J. Steinlechner, 47, 48 S. Steinlechner, 47, 48 N. Stergioulas, 287 P. Stevens, 51 M. StPierre, 177 L. C. Strang, 146 G. Stratta, 307, 308, 75, 309 M. D. Strong, 28 A. Strunk, 21 R. Sturani, 310 A. L. Stuver, 110 M. Suchenek, 101 S. Sudhagar, 32, 101 N. Sueltmann, 92 A. G. Sullivan, 176 K. D. Sullivan, 28 L. Sun, 29 S. Sunil, 99 A. Sur, 101 J. Suresh, 52, 131 P. J. Sutton, 36 Takamasa Suzuki, 226 Takanori Suzuki, 20 B. L. Swinkels, 48 A. Syx, 76 M. J. Szczepa'nczyk, 56 P. Szewczyk, 133 M. Tacca, 48 H. Tagoshi, 169 S. C. Tait, 42 H. Takahashi, 311 R. Takahashi, 38 A. Takamori, 65 K. Takatani, 207 H. Takeda, 312 M. Takeda, 207 C. J. Talbot, 106 C. Talbot, 46 M. Tamaki, 169 N. Tamanini, 137 D. Tanabe, 159 K. Tanaka, 313 S. J. Tanaka, 271 T. Tanaka, 312 A. J. Tanasijczuk, 131 D. Tang, 43 S. Tanioka, 85 D. B. Tanner, 56 L. Tao, 56 R. D. Tapia, 26 E. N. Tapia San Mart'ın, 48 R. Tarafder, 19 C. Taranto, 149, 102 A. Taruya, 314 J. D. Tasson, 190 M. Teloi, 49  \nR. Tenorio, 107 H. Themann, 210 A. Theodoropoulos, 152 M. P. Thirugnanasambandam, 32 L. M. Thomas, 126 M. Thomas, 74 P. Thomas, 21 J. E. Thompson, 165 S. R. Thondapu, 111 K. A. Thorne, 74 E. Thrane, 25 J. Tissino, 55 A. Tiwari, 32 Shubhanshu Tiwari, 194 Srishti Tiwari, 32 V. Tiwari, 126 M. R. Todd, 85 A. M. Toivonen, 105 K. Toland, 42 A. E. Tolley, 138 T. Tomaru, 38 K. Tomita, 207 T. Tomura, 61 C. Tong-Yu, 159 A. Toriyama, 271 N. Toropov, 126 A. Torres-Forn'e, 152, 153 C. I. Torrie, 19 M. Toscani, 137 I. Tosta e Melo, 315 E. Tournefier, 44 A. A. Trani, 52 A. Trapananti, 63, 62 F. Travasso, 63, 62 G. Traylor, 74 J. Trenado, 53 M. Trevor, 134 M. C. Tringali, 68 A. Tripathee, 95 L. Troiano, 316, 121 A. Trovato, 58, 266 L. Trozzo, 23 R. J. Trudeau, 19 T. T. L. Tsang, 36 R. Tso, 165, † S. Tsuchida, 317 L. Tsukada, 26 T. Tsutsui, 52 K. Turbang, 193, 122 M. Turconi, 60 C. Turski, 100 H. Ubach, 53, 87 A. S. Ubhi, 126 N. Uchikata, 169 T. Uchiyama, 61 R. P. Udall, 19 T. Uehara, 318 K. Ueno, 52 C. S. Unnikrishnan, 269 T. Ushiba, 61 A. Utina, 47, 48 M. Vacatello, 37, 93 H. Vahlbruch, 30, 31 N. Vaidya, 19 G. Vajente, 19 A. Vajpeyi, 25 G. Valdes, 145 J. Valencia, 107 M. Valentini, 115, 48 S. A. Vallejo-Pe˜na, 293 S. Vallero, 41 V. Valsan, 27 N. van Bakel, 48 M. van Beuzekom, 48 M. van Dael, 48, 319 J. F. J. van den Brand, 47, 115, 48 C. Van Den Broeck, 83, 48 D. C. Vander-Hyde, 85 M. van der Sluys, 48, 83 A. Van de Walle, 51 J. van Dongen, 48, 115 K. Vandra, 110 H. van Haevermaet, 122 J. V. van Heijningen, 131 J. Vanosky, 19 M. H. P. M. van Putten, 320 Z. van Ranst, 47, 48 N. van Remortel, 122 M. Vardaro, 47, 48 A. F. Vargas, 146 V. Varma, 18 M. Vas'uth, 89 A. Vecchio, 126 G. Vedovato, 97 J. Veitch, 42 P. J. Veitch, 104 S. Venikoudis, 131 J. Venneberg, 30, 31 P. Verdier, 170 D. Verkindt, 44 B. Verma, 151 P. Verma, 191 Y. Verma, 111 S. M. Vermeulen, 19 D. Veske, 176 F. Vetrano, 72 A. Veutro, 75 A. M. Vibhute, 21 A. Vicer'e, 72, 73 S. Vidyant, 85 A. D. Viets, 321 A. Vijaykumar, 91 A. Vilkha, 221 V. Villa-Ortega, 140 E. T. Vincent, 69 J.-Y. Vinet, 60 S. Viret, 170 A. Virtuoso, 266, 58 S. Vitale, 46 H. Vocca, 94, 62 D. Voigt, 92 E. R. G. von Reis, 21 J. S. A. von Wrangel, 30, 31 S. P. Vyatchanin, 116 L. E. Wade, 82 M. Wade, 82 K. J. Wagner, 221 R. C. Walet, 48 M. Walker, 132 G. S. Wallace, 106 L. Wallace, 19 H. Wang, 284 J. Z. Wang, 95 W. H. Wang, 178 Z. Wang, 159 G. Waratkar, 156 R. L. Ward, 29 J. Warner, 21 M. Was, 44 T. Washimi, 38 N. Y. Washington, 19 D. Watarai, 52 K. E. Wayt, 82 B. Weaver, 21 C. R. Weaving, 138 S. A. Webster, 42 M. Weinert, 30, 31 A. J. Weinstein, 19 R. Weiss, 46 C. M. Weller, 64 R. A. Weller, 208 F. Wellmann, 30, 31 L. Wen, 43 P. Weßels, 30, 31 K. Wette, 29 J. T. Whelan, 221 D. D. White, 66 B. F. Whiting, 56 C. Whittle, 46 J. B. Wildberger, 18 O. S. Wilk, 82 D. Wilken, 30, 31, 31 K. Willetts, 36 D. Williams, 42 M. J. Williams, 42 N. S. Williams, 126 J. L. Willis, 19 B. Willke, 31, 30, 31 M. Wils, 117 C. C. Wipf, 19 G. Woan, 42 J. Woehler, 47, 48 J. K. Wofford, 221 N. E. Wolfe, 46 D. Wong, 155 H. T. Wong, 159 H. W. Y. Wong, 154 I. C. F. Wong, 154 J. L. Wright, 29 M. Wright, 42 C. Wu, 158 D. S. Wu, 30, 31 H. Wu, 158 D. M. Wysocki, 27 L. Xiao, 19 V. A. Xu, 46 Y. Xu, 194 N. Yadav, 101 H. Yamamoto, 19 K. Yamamoto, 168 M. Yamamoto, 168 T. S. Yamamoto, 253 T. Yamamoto, 61 S. Yamamura, 169 R. Yamazaki, 271 S. Yan, 35 T. Yan, 126 F. W. Yang, 204 F. Yang, 176 K. Z. Yang, 105 L.-C. Yang, 162 Y. Yang, 162 Z. Yarbrough, 28 S.-W. Yeh, 158 A. B. Yelikar, 221 S. M. C. Yeung, 27 X. Yin, 46 J. Yokoyama, 52 T. Yokozawa, 61 J. Yoo, 166 H. Yu, 165 H. Yuzurihara, 61 A. Zadro˙zny, 191 A. J. Zannelli, 132 M. Zanolin, 59 M. Zeeshan, 221 T. Zelenova, 68 J.-P. Zendri, 97 M. Zeoli, 127, 131 M. Zerrad, 50, 243 M. Zevin, 86 A. C. Zhang, 176 J. Zhang, 29 L. Zhang, 19 R. Zhang, 56 T. Zhang, 126 Y. Zhang, 29 C. Zhao, 43 Yue Zhao, 204 Yuhang Zhao, 169, 38, 78 Y. Zheng, 113 H. Zhong, 105 S. Zhong, 43 R. Zhou, 223 Z.-H. Zhu, 144, 322 A. B. Zimmerman, 164 M. E. Zucker, 46, 19 J. Zweizig, 19  \n- 1 Department of Astronomy and Astrophysics, The Pennsylvania State University, 525 Davey Lab, University Park, PA 16802, USA\n- 2 Department of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487, USA 3 Department of Astronomy & Astrophysics, University of Toronto, Toronto, ON M5S 3H4 4 Dunlap Institute for Astronomy & Astrophysics, University of Toronto, Toronto, ON M5S 3H4 5 Astrophysics Science Division, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA 6 INAF - IASF-Palermo, via Ugo La Malfa 153, 90146 Palermo PA, Italy 7 INAF-Osservatorio Astronomico di Brera, Via E. Bianchi 46, 23807 Merate, LC, Italy 8 Space Science Data Center (SSDC) - Agenzia Spaziale Italiana (ASI), 00133 Roma, Italy 9 INAF - Osservatorio Astronomico di Roma, Via Frascati 33, 00040 Monte Porzio Catone, Italy 10 MIFT Department, Polo Papardo, University of Messina, Viale Ferdinando Stagno d'Alcontres, 31, 98166 Messina, Italy 11 School of Physics and Astronomy, University of Leicester, University Road, Leicester LE1 7RH, UK 12 Department of Physics & Astronomy, Clemson University, Kinard Lab of Physics, Clemson, SC 29634, USA 13 Mullard Space Science Laboratory, University College London, Holmbury St. Mary, Dorking, Surrey RH5 6NT, UK 14 INAF-Osservatorio Astronomico di Roma, Via di Frascati 33, 00040 Monte Porzio Catone, RM, Italy 15 Los Alamos National Laboratory, PO Box 1663, Los Alamos New Mexico 87545 16 INAF - Osservatorio Astronomico di Brera, Via E. Bianchi 46, 23807 Merate, Italy 17 University of Rome Tor Vergata, via Cracovia 50, 00100 Roma, Italy 18 Max Planck Institute for Gravitational Physics (Albert Einstein Institute), D-14476 Potsdam, Germany 19 LIGO Laboratory, California Institute of Technology, Pasadena, CA 91125, USA 20 Graduate School of Science, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8551, Japan 21 LIGO Hanford Observatory, Richland, WA 99352, USA  \n22 Dipartimento di Farmacia, Universit'a di Salerno, I-84084 Fisciano, Salerno, Italy 23  \nINFN, Sezione di Napoli, I-80126 Napoli, Italy  \n54  \n61  \n53  \n24 University of Warwick, Coventry CV4 7AL, United Kingdom  \n25 OzGrav, School of Physics & Astronomy, Monash University, Clayton 3800, Victoria, Australia  \n26 The Pennsylvania State University, University Park, PA 16802, USA  \n27 University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA  \n28 Louisiana State University, Baton Rouge, LA 70803, USA  \n29 OzGrav, Australian National University, Canberra, Australian Capital Territory 0200, Australia  \nMax Planck Institute for Gravitational Physics (Albert Einstein Institute), D-30167 Hannover, Germany  \n31 Leibniz Universitat Hannover, D-30167 Hannover, Germany  \n32  \nInter-University Centre for Astronomy and Astrophysics, Pune 411007, India  \n33 University of Cambridge, Cambridge CB2 1TN, United Kingdom  \n34 Instituto Nacional de Pesquisas Espaciais, 12227-010 S˜ao Jos'e dos Campos, S˜ao Paulo, Brazil  \n35 Stanford University, Stanford, CA 94305, USA  \n36 Cardiff University, Cardiff CF24 3AA, United Kingdom  \n37 INFN, Sezione di Pisa, I-56127 Pisa, Italy  \n38 Gravitational Wave Science Project, National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka City, Tokyo 181-8588, Japan  \n39 Advanced Technology Center, National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka City, Tokyo 181-8588, Japan  \n40 Dipartimento di Fisica, Universit'a degli Studi di Torino, I-10125 Torino, Italy  \n41 INFN Sezione di Torino, I-10125 Torino, Italy  \n42 SUPA, University of Glasgow, Glasgow G12 8QQ, United Kingdom  \n43 OzGrav, University of Western Australia, Crawley, Western Australia 6009, Australia  \n44 Univ. Savoie Mont Blanc, CNRS, Laboratoire d'Annecy de Physique des Particules - IN2P3, F-74000 Annecy, France  \n45 Universit'a di Napoli 'Federico II', I-80126 Napoli, Italy  \n46 LIGO Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  \n47 Maastricht University, 6200 MD Maastricht, Netherlands  \n48 Nikhef, 1098 XG Amsterdam, Netherlands  \n49 Universit'e Libre de Bruxelles, Brussels 1050, Belgium  \n50 Institut Fresnel, Aix Marseille Universit'e, CNRS, Centrale Marseille, F-13013 Marseille, France  \n51 Universit'e Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France  \n52 University of Tokyo, Tokyo, 113-0033, Japan.  \nInstitut de Ci'encies del Cosmos (ICCUB), Universitat de Barcelona (UB), c. Mart'ı i Franqu'es, 1, 08028 Barcelona, Spain  \nInstitut de F'ısica d'Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, E-08193 Bellaterra  \n(Barcelona), Spain  \n55 Gran Sasso Science Institute (GSSI), I-67100 L'Aquila, Italy  \n56 University of Florida, Gainesville, FL 32611, USA  \n57 Dipartimento di Scienze Matematiche, Informatiche e Fisiche, Universit'a di Udine, I-33100 Udine, Italy  \n58 INFN, Sezione di Trieste, I-34127 Trieste, Italy  \n59 Embry-Riddle Aeronautical University, Prescott, AZ 86301, USA  \n60 Universit'e Cˆote d'Azur, Observatoire de la Cˆote d'Azur, CNRS, Artemis, F-06304 Nice, France  \nInstitute for Cosmic Ray Research, KAGRA Observatory, The University of Tokyo, 238 Higashi-Mozumi, Kamioka-cho, Hida City,  \nGifu 506-1205, Japan  \n62  \nINFN, Sezione di Perugia, I-06123 Perugia, Italy  \n63 Universit'a di Camerino, I-62032 Camerino, Italy  \n64 University of Washington, Seattle, WA 98195, USA  \n65 Earthquake Research Institute, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan  \n66  \nCalifornia State University Fullerton, Fullerton, CA 92831, USA  \n67 INFN, Sezione di Genova, I-16146 Genova, Italy  \n68 European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy  \n69 Georgia Institute of Technology, Atlanta, GA 30332, USA  \n70 Royal Holloway, University of London, London TW20 0EX, United Kingdom  \n71 The Graduate University for Advanced Studies (SOKENDAI), 2-21-1 Osawa, Mitaka City, Tokyo 181-8588, Japan  \n72 Universit'a degli Studi di Urbino 'Carlo Bo', I-61029 Urbino, Italy  \n73 INFN, Sezione di Firenze, I-50019 Sesto Fiorentino, Firenze, Italy  \n74 LIGO Livingston Observatory, Livingston, LA 70754, USA  \n75 INFN, Sezione di Roma, I-00185 Roma, Italy  \n76 Universit'e de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France  \n77 Dipartimento di Fisica 'E.R. Caianiello', Universit'a di Salerno, I-84084 Fisciano, Salerno, Italy  \n78 Universit'e Paris Cit'e, CNRS, Astroparticule et Cosmologie, F-75013 Paris, France  \n30  \n115  \n80  \n119  \n79 King's College London, University of London, London WC2R 2LS, United Kingdom  \nKorea Institute of Science and Technology Information, Daejeon 34141, Republic of Korea  \n81 Universit'e libre de Bruxelles, 1050 Bruxelles, Belgium  \n82 Kenyon College, Gambier, OH 43022, USA  \n83 Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University, 3584 CC Utrecht, Netherlands  \n84 University of Oregon, Eugene, OR 97403, USA  \n85 Syracuse University, Syracuse, NY 13244, USA  \n86 Northwestern University, Evanston, IL 60208, USA  \n- 87 Departament de F'ısica Qu'antica i Astrof'ısica (FQA), Universitat de Barcelona (UB), c. Mart'ı i Franqu'es, 1, 08028 Barcelona, Spain  \n88 Dipartimento di Medicina, Chirurgia e Odontoiatria 'Scuola Medica Salernitana', Universit'a di Salerno, I-84081 Baronissi, Salerno,  \nItaly  \n89 Wigner RCP, RMKI, H-1121 Budapest, Hungary  \n90 NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA  \n91 International Centre for Theoretical Sciences, Tata Institute of Fundamental Research, Bengaluru 560089, India  \n92 Universitat Hamburg, D-22761 Hamburg, Germany  \n93 Universit'a di Pisa, I-56127 Pisa, Italy  \n94 Universit'a di Perugia, I-06123 Perugia, Italy  \n95 University of Michigan, Ann Arbor, MI 48109, USA  \nUniversit'a di Padova, Dipartimento di Fisica e Astronomia, I-35131 Padova, Italy  \n97 INFN, Sezione di Padova, I-35131 Padova, Italy  \n98 Montana State University, Bozeman, MT 59717, USA  \n99 Institute for Plasma Research, Bhat, Gandhinagar 382428, India  \n100 Universiteit Gent, B-9000 Gent, Belgium  \n101 Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, 00-716, Warsaw, Poland  \n102 INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy  \n103 Dipartimento di Ingegneria, Universit'a del Sannio, I-82100 Benevento, Italy  \n104 OzGrav, University of Adelaide, Adelaide, South Australia 5005, Australia  \n105 University of Minnesota, Minneapolis, MN 55455, USA  \n106 SUPA, University of Strathclyde, Glasgow G1 1XQ, United Kingdom  \n107 IAC3-IEEC, Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain  \n108 Departamento de Matem'aticas, Universitat Aut'onoma de Barcelona, 08193 Bellaterra (Barcelona), Spain  \n109 Theoretisch-Physikalisches Institut, Friedrich-Schiller-Universitat Jena, D-07743 Jena, Germany  \n110 Villanova University, Villanova, PA 19085, USA  \n111 RRCAT, Indore, Madhya Pradesh 452013, India  \n112 GRAPPA, Anton Pannekoek Institute for Astronomy and Institute for High-Energy Physics, University of Amsterdam, 1098 XH Amsterdam, Netherlands  \n113 Missouri University of Science and Technology, Rolla, MO 65409, USA  \n114 Colorado State University, Fort Collins, CO 80523, USA  \nDepartment of Physics and Astronomy, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands  \n116 Lomonosov Moscow State University, Moscow 119991, Russia  \n117 Katholieke Universiteit Leuven, Oude Markt 13, 3000 Leuven, Belgium  \n118 Universit'a di Trento, Dipartimento di Fisica, I-38123 Povo, Trento, Italy  \nINFN, Trento Institute for Fundamental Physics and Applications, I-38123 Povo, Trento, Italy  \n120 Bar-Ilan University, Ramat Gan, 5290002, Israel  \n121  \nINFN, Sezione di Napoli, Gruppo Collegato di Salerno, I-80126 Napoli, Italy  \n122 Universiteit Antwerpen, 2000 Antwerpen, Belgium  \n123 Universit'a di Roma 'La Sapienza', I-00185 Roma, Italy  \n124 Centre national de la recherche scientifique, 75016 Paris, France  \n125 Univ Rennes, CNRS, Institut FOTON - UMR 6082, F-35000 Rennes, France  \n126 University of Birmingham, Birmingham B15 2TT, United Kingdom  \n127 Universit'e de Li'ege, B-4000 Li'ege, Belgium  \n128 Instituto de Fisica Teorica UAM-CSIC, Universidad Autonoma de Madrid, 28049 Madrid, Spain  \n129 INFN, Laboratori Nazionali del Gran Sasso, I-67100 Assergi, Italy  \n130 Laboratoire Kastler Brossel, Sorbonne Universit'e, CNRS, ENS-Universit'e PSL, Coll'ege de France, F-75005 Paris, France  \n131  \nUniversit'e catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium  \n132 Christopher Newport University, Newport News, VA 23606, USA  \n133 Astronomical Observatory Warsaw University, 00-478 Warsaw, Poland  \n96  \n184  \n137  \n144  \n150  \n134 University of Maryland, College Park, MD 20742, USA  \n135 Universit'a degli Studi di Milano-Bicocca, I-20126 Milano, Italy  \n136 INFN, Sezione di Milano-Bicocca, I-20126 Milano, Italy  \nL2IT, Laboratoire des 2 Infinis - Toulouse, Universit'e de Toulouse, CNRS/IN2P3, UPS, F-31062 Toulouse Cedex 9, France  \n138 University of Portsmouth, Portsmouth, PO1 3FX, United Kingdom  \n139  \nUniversit'e de Lyon, Universit'e Claude Bernard Lyon 1, CNRS, Institut Lumi'ere Mati'ere, F-69622 Villeurbanne, France  \n140 IGFAE, Universidade de Santiago de Compostela, 15782 Spain  \n141 University of Chicago, Chicago, IL 60637, USA  \n142 Dipartimento di Fisica, Universit'a degli Studi di Genova, I-16146 Genova, Italy  \n143 University of California, Riverside, Riverside, CA 92521, USA  \nDepartment of Astronomy, Beijing Normal University, Xinjiekouwai Street 19, Haidian District, Beijing 100875, China  \n145 Texas A&M University, College Station, TX 77843, USA  \n146 OzGrav, University of Melbourne, Parkville, Victoria 3010, Australia  \n147 INFN, Laboratori Nazionali del Sud, I-95125 Catania, Italy  \n148 Niels Bohr Institute, Copenhagen University, 2100 København, Denmark  \n149 Universit'a di Roma Tor Vergata, I-00133 Roma, Italy  \nUniversity of Sannio at Benevento, I-82100 Benevento, Italy and INFN, Sezione di Napoli, I-80100 Napoli, Italy  \n151 University of Massachusetts Dartmouth, North Dartmouth, MA 02747, USA  \n152 Departamento de Astronom'ıa y Astrof'ısica, Universitat de Val'encia, E-46100 Burjassot, Val'encia, Spain  \n153 Observatori Astron'omic, Universitat de Val'encia, E-46980 Paterna, Val'encia, Spain  \n154 The Chinese University of Hong Kong, Shatin, NT, Hong Kong  \n155 University of British Columbia, Vancouver, BC V6T 1Z4, Canada  \n156 Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India  \n157 Department of Physics, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan  \n158 National Tsing Hua University, Hsinchu City 30013, Taiwan  \n159 National Central University, Taoyuan City 320317, Taiwan  \n160 OzGrav, Charles Sturt University, Wagga Wagga, New South Wales 2678, Australia  \n161 Queen Mary University of London, London E1 4NS, United Kingdom  \n162 Department of Electrophysics, National Yang Ming Chiao Tung University, 101 Univ. Street, Hsinchu, Taiwan  \n163 Kamioka Branch, National Astronomical Observatory of Japan, 238 Higashi-Mozumi, Kamioka-cho, Hida City, Gifu 506-1205, Japan  \n164  \nUniversity of Texas, Austin, TX 78712, USA  \n165 CaRT, California Institute of Technology, Pasadena, CA 91125, USA  \n166 Cornell University, Ithaca, NY 14850, USA  \n167 Dipartimento di Ingegneria Industriale (DIIN), Universit'a di Salerno, I-84084 Fisciano, Salerno, Italy  \n168 Faculty of Science, University of Toyama, 3190 Gofuku, Toyama City, Toyama 930-8555, Japan  \n169 Institute for Cosmic Ray Research, KAGRA Observatory, The University of Tokyo, 5-1-5 Kashiwa-no-Ha, Kashiwa City, Chiba 277-8582, Japan  \n170 Universit'e Lyon, Universit'e Claude Bernard Lyon 1, CNRS, IP2I Lyon / IN2P3, UMR 5822, F-69622 Villeurbanne, France  \n171 INAF, Osservatorio Astronomico di Padova, I-35122 Padova, Italy  \n172 OzGrav, Swinburne University of Technology, Hawthorn VIC 3122, Australia  \n173 INAF, Osservatorio Astronomico di Brera sede di Merate, I-23807 Merate, Lecco, Italy  \n174 Departamento de Matem'aticas, Universitat de Val'encia, E-46100 Burjassot, Val'encia, Spain  \n175 Texas Tech University, Lubbock, TX 79409, USA  \n176 Columbia University, New York, NY 10027, USA  \n177 University of Rhode Island, Kingston, RI 02881, USA  \n178 The University of Texas Rio Grande Valley, Brownsville, TX 78520, USA  \n179 Bellevue College, Bellevue, WA 98007, USA  \n180 Scuola Normale Superiore, I-56126 Pisa, Italy  \n181 Chennai Mathematical Institute, Chennai 603103, India  \n182 Universit'a degli Studi di Sassari, I-07100 Sassari, Italy  \n183 The University of Sheffield, Sheffield S10 2TN, United Kingdom  \nUniversit'e Lyon, Universit'e Claude Bernard Lyon 1, CNRS, Laboratoire des Mat'eriaux Avanc'es (LMA), IP2I Lyon / IN2P3, UMR  \n5822, F-69622 Villeurbanne, France  \n185 Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Universit'a di Parma, I-43124 Parma, Italy  \n186  \nINFN, Sezione di Milano Bicocca, Gruppo Collegato di Parma, I-43124 Parma, Italy  \n187  \nPerimeter Institute, Waterloo, ON N2L 2Y5, Canada  \n188 Corps des Mines, Mines Paris, Universit'e PSL, 60 Bd Saint-Michel, 75272 Paris, France  \n235  \n189 Indian Institute of Technology Madras, Chennai 600036, India  \n190 Carleton College, Northfield, MN 55057, USA  \n191 National Center for Nuclear Research, 05-400 ' Swierk-Otwock, Poland  \n192 Institut d'Astrophysique de Paris, Sorbonne Universit'e, CNRS, UMR 7095, 75014 Paris, France  \n193 Vrije Universiteit Brussel, 1050 Brussel, Belgium  \n194 University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland  \nCanadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON M5S 3H8, Canada  \n196 Stony Brook University, Stony Brook, NY 11794, USA  \n197 Center for Computational Astrophysics, Flatiron Institute, New York, NY 10010, USA  \n198 Montclair State University, Montclair, NJ 07043, USA  \n199 Institute for Nuclear Research, H-4026 Debrecen, Hungary  \n200 CNR-SPIN, I-84084 Fisciano, Salerno, Italy  \n201 Scuola di Ingegneria, Universit'a della Basilicata, I-85100 Potenza, Italy  \n202 Western Washington University, Bellingham, WA 98225, USA  \n203 SUPA, University of the West of Scotland, Paisley PA1 2BE, United Kingdom  \n204 The University of Utah, Salt Lake City, UT 84112, USA  \n205 Eotvos University, Budapest 1117, Hungary  \n206 Centro de F'ısica das Universidades do Minho e do Porto, Universidade do Minho, PT-4710-057 Braga, Portugal  \n207 Department of Physics, Graduate School of Science, Osaka Metropolitan University, 3-3-138 Sugimoto-cho, Sumiyoshi-ku, Osaka City, Osaka 558-8585, Japan  \n208 Vanderbilt University, Nashville, TN 37235, USA  \n209 Universit'e Cˆote d'Azur, Observatoire de la Cˆote d'Azur, CNRS, Lagrange, F-06304 Nice, France  \n210 California State University, Los Angeles, Los Angeles, CA 90032, USA  \n211 University of Szeged, D'om t'er 9, Szeged 6720, Hungary  \n212 INAF, Osservatorio Astronomico di Capodimonte, I-80131 Napoli, Italy  \n213  \nAriel University, Ramat HaGolan St 65, Ari'el, Israel  \n214 Universit'e de Normandie, ENSICAEN, UNICAEN, CNRS/IN2P3, LPC Caen, F-14000 Caen, France  \n215  \nLaboratoire de Physique Corpusculaire Caen, 6 boulevard du mar'echal Juin, F-14050 Caen, France  \n216 The University of Mississippi, University, MS 38677, USA  \n217 Institute of Physics, Academia Sinica, 128 Sec. 2, Academia Rd., Nankang, Taipei 11529, Taiwan  \nShanghai Astronomical Observatory, Chinese Academy of Sciences, 80 Nandan Road, Shanghai 200030, China  \n219 American University, Washington, DC 20016, USA  \n220 University of Nevada, Las Vegas, Las Vegas, NV 89154, USA  \n221 Rochester Institute of Technology, Rochester, NY 14623, USA  \n222 Department of Applied Physics, Fukuoka University, 8-19-1 Nanakuma, Jonan, Fukuoka City, Fukuoka 814-0180, Japan 223 University of California, Berkeley, CA 94720, USA  \n224 University of Lancaster, Lancaster LA1 4YW, United Kingdom  \n225 College of Industrial Technology, Nihon University, 1-2-1 Izumi, Narashino City, Chiba 275-8575, Japan 226 Faculty of Engineering, Niigata University, 8050 Ikarashi-2-no-cho, Nishi-ku, Niigata City, Niigata 950-2181, Japan 227 Department of Physics, Tamkang University, No. 151, Yingzhuan Rd., Danshui Dist., New Taipei City 25137, Taiwan 228 Rutherford Appleton Laboratory, Didcot OX11 0DE, United Kingdom  \n229 Department of Astronomy and Space Science, Chungnam National University, 9 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea  \n230 Institute of Applied Physics, Nizhny Novgorod, 603950, Russia  \n231  \n232  \nKavli Institute for Astronomy and Astrophysics, Peking University, Yiheyuan Road 5, Haidian District, Beijing 100871, China  \nNambu Yoichiro Institute of Theoretical and Experimental Physics (NITEP), Osaka Metropolitan University, 3-3-138 Sugimoto-cho,  \nSumiyoshi-ku, Osaka City, Osaka 558-8585, Japan  \n233 Directorate of Construction, Services & Estate Management, Mumbai 400094, India  \n234 University of Biaglyph[suppress]lystok, 15-424 Biaglyph[suppress]lystok, Poland  \nNational Astronomical Observatories, Chinese Academic of Sciences, 20A Datun Road, Chaoyang District, Beijing, China  \n- 236 School of Astronomy and Space Science, University of Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing, China  \n237 University of Southampton, Southampton SO17 1BJ, United Kingdom  \n- 238 Department of Physics, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, Republic of Korea  \n239 Institute for Cosmic Ray Research, The University of Tokyo, 5-1-5 Kashiwa-no-Ha, Kashiwa City, Chiba 277-8582, Japan 240 Institute for High-Energy Physics, University of Amsterdam, 1098 XH Amsterdam, Netherlands  \n218  \n195  \n244  \n259  \n264  \n267  \n271  \n282  \n241 Chung-Ang University, Seoul 06974, Republic of Korea  \n242 University of Washington Bothell, Bothell, WA 98011, USA  \n243 Aix Marseille Universit'e, Jardin du Pharo, 58 Boulevard Charles Livon, 13007 Marseille, France  \nLaboratoire de Physique et de Chimie de l'Environnement, Universit'e Joseph KI-ZERBO, 9GH2+3V5, Ouagadougou, Burkina Faso  \n245 Ewha Womans University, Seoul 03760, Republic of Korea  \n246 Seoul National University, Seoul 08826, Republic of Korea  \n247 Korea Astronomy and Space Science Institute, Daejeon 34055, Republic of Korea  \n248 Sungkyunkwan University, Seoul 03063, Republic of Korea  \n249 National Institute for Mathematical Sciences, Daejeon 34047, Republic of Korea  \n250 Institute of Particle and Nuclear Studies (IPNS), High Energy Accelerator Research Organization (KEK), 1-1 Oho, Tsukuba City, Ibaraki 305-0801, Japan  \n251 Bard College, Annandale-On-Hudson, NY 12504, USA  \n252 Institute of Mathematics, Polish Academy of Sciences, 00656 Warsaw, Poland  \n253 Department of Physics, Nagoya University, ES building, Furocho, Chikusa-ku, Nagoya, Aichi 464-8602, Japan  \n254 Universit'e de Montr'eal/Polytechnique, Montreal, Quebec H3T 1J4, Canada  \n255 Inje University Gimhae, South Gyeongsang 50834, Republic of Korea  \n256  \nNAVIER,  \n'  \nEcole des Ponts, Univ Gustave Eiffel, CNRS, Marne-la-Vall'ee, France  \n257 Universit'a di Firenze, Sesto Fiorentino I-50019, Italy  \n258 Department of Physics, University of Trento, via Sommarive 14, Povo, 38123 TN, Italy  \nNational Center for High-performance computing, National Applied Research Laboratories, No. 7, R&D 6th Rd., Hsinchu Science  \nPark, Hsinchu City 30076, Taiwan  \n260 NASA Marshall Space Flight Center, Huntsville, AL 35811, USA  \n261 West Virginia University, Morgantown, WV 26506, USA  \n262 School of Physics Science and Engineering, Tongji University, Shanghai 200092, China  \n263 Institut d'Estudis Espacials de Catalunya, c. Gran Capit'a, 2-4, 08034 Barcelona, Spain  \nInstitucio Catalana de Recerca i Estudis Avan¸cats (ICREA), Passeig de Llu'ıs Companys, 23, 08010 Barcelona, Spain  \n265 Tsinghua University, Beijing 100084, China  \n266 Dipartimento di Fisica, Universit'a di Trieste, I-34127 Trieste, Italy  \nInstitute for Photon Science and Technology, The University of Tokyo, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-8656, Japan  \n268  \nINFN Cagliari, Physics Department, Universit'a degli Studi di Cagliari, Cagliari 09042, Italy  \n269 Tata Institute of Fundamental Research, Mumbai 400005, India  \n270 Hobart and William Smith Colleges, Geneva, NY 14456, USA  \nDepartment of Physical Sciences, Aoyama Gakuin University, 5-10-1 Fuchinobe, Sagamihara City, Kanagawa 252-5258, Japan  \n272  \nInstitut des Hautes Etudes Scientifiques, F-91440 Bures-sur-Yvette, France  \nFaculty of Law, Ryukoku University, 67 Fukakusa Tsukamoto-cho, Fushimi-ku, Kyoto City, Kyoto 612-8577, Japan  \n274  \nIndian Institute of Science Education and Research, Kolkata, Mohanpur, West Bengal 741252, India  \n275 Department of Physics and Astronomy, University of Notre Dame, 225 Nieuwland Science Hall, Notre Dame, IN 46556, USA 276 University of Stavanger, 4021 Stavanger, Norway  \n277  \nDepartment of Astronomy, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan  \n278  \nLaboratoire Univers et Th'eories, Observatoire de Paris, 92190 Meudon, France  \n279 Observatoire de Paris, 75014 Paris, France  \n280 Universit'e PSL, 75006 Paris, France  \n281 Universit'e de Paris Cit'e, 75006 Paris, France  \nGraduate School of Science and Technology, Niigata University, 8050 Ikarashi-2-no-cho, Nishi-ku, Niigata City, Niigata 950-2181,  \nJapan  \n283 Niigata Study Center, The Open University of Japan, 754 Ichibancho, Asahimachi-dori, Chuo-ku, Niigata City, Niigata 951-8122, Japan  \n284 Department of Physics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan  \n285  \nCSIR-Central Glass and Ceramic Research Institute, Kolkata, West Bengal 700032, India  \n286  \nConsiglio Nazionale delle Ricerche - Istituto dei Sistemi Complessi, I-00185 Roma, Italy  \n287 Department of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece  \n288  \nMuseo Storico della Fisica e Centro Studi e Ricerche 'Enrico Fermi', I-00184 Roma, Italy  \n289 Dipartimento di Ingegneria Industriale, Elettronica e Meccanica, Universit'a degli Studi Roma Tre, I-00146 Roma, Italy  \n290 INFN, Sezione di Roma Tre, I-00146 Roma, Italy  \n291 Universit'a di Trento, Dipartimento di Matematica, I-38123 Povo, Trento, Italy  \nSubatech, CNRS/IN2P3 - IMT Atlantique - Nantes Universit'e, 4 rue Alfred Kastler BP 20722 44307 Nantes C  \n293 Universidad de Antioquia, Medell'ın, Colombia  \n292  \n273  \n'  \nEDEX 03, France  \n303  \n307  \n294 Departamento de F'ısica - ETSIDI, Universidad Polit'ecnica de Madrid, 28012 Madrid, Spain  \n295 Department of Electronic Control Engineering, National Institute of Technology, Nagaoka College, 888 Nishikatakai, Nagaoka City, Niigata 940-8532, Japan  \n- 296 Departamento de Matem'atica da Universidade de Aveiro and Centre for Research and Development in Mathematics and Applications, 3810-183 Aveiro, Portugal  \n297 Marquette University, Milwaukee, WI 53233, USA  \nFaculty of Science, Toho University, 2-2-1 Miyama, Funabashi City, Chiba 274-8510, Japan  \nIndian Institute of Technology, Palaj, Gandhinagar, Gujarat 382355, India  \n298 299  \n300 Laboratoire MSME, Cit'e Descartes, 5 Boulevard Descartes, Champs-sur-Marne, 77454 Marne-la-Vall'ee Cedex 2, France  \n301  \nGraduate School of Science and Technology, Gunma University, 4-2 Aramaki, Maebashi, Gunma 371-8510, Japan  \n302 Institute for Quantum Studies, Chapman University, 1 University Dr., Orange, CA 92866, USA  \nFaculty of Information Science and Technology, Osaka Institute of Technology, 1-79-1 Kitayama, Hirakata City, Osaka 573-0196,  \nJapan  \n304 INAF, Osservatorio Astrofisico di Arcetri, I-50125 Firenze, Italy  \n305 Indian Institute of Technology Hyderabad, Sangareddy, Khandi, Telangana 502285, India  \n306 Indian Institute of Science Education and Research, Pune, Maharashtra 411008, India  \nInstitut fur Theoretische Physik, Johann Wolfgang Goethe-Universitat, Max-von-Laue-Str. 1, 60438 Frankfurt am Main, Germany  \n308  \nIstituto di Astrofisica e Planetologia Spaziali di Roma, 00133 Roma, Italy  \n309 INAF, Osservatorio di Astrofisica e Scienza dello Spazio, I-40129 Bologna, Italy  \n310 Universidade Estadual Paulista, 01140-070 Campinas, S˜ao Paulo, Brazil  \n- 311 Research Center for Space Science, Advanced Research Laboratories, Tokyo City University, 8-15-1 Todoroki, Setagaya, Tokyo 158-0082, Japan\n- 312 Department of Physics, Kyoto University, Kita-Shirakawa Oiwake-cho, Sakyou-ku, Kyoto City, Kyoto 606-8502, Japan\n- 313 Institute for Cosmic Ray Research, Research Center for Cosmic Neutrinos, The University of Tokyo, 5-1-5 Kashiwa-no-Ha, Kashiwa City, Chiba 277-8582, Japan\n- 314 Yukawa Institute for Theoretical Physics (YITP), Kyoto University, Kita-Shirakawa Oiwake-cho, Sakyou-ku, Kyoto City, Kyoto 606-8502, Japan  \n315 University of Catania, Department of Physics and Astronomy, Via S. Sofia, 64, 95123 Catania CT, Italy  \n- 316 Dipartimento di Scienze Aziendali - Management and Innovation Systems (DISA-MIS), Universit'a di Salerno, I-84084 Fisciano, Salerno, Italy  \n317 National Institute of Technology, Fukui College, Geshi-cho, Sabae-shi, Fukui 916-8507, Japan  \n- 318 Department of Communications Engineering, National Defense Academy of Japan, 1-10-20 Hashirimizu, Yokosuka City, Kanagawa 239-8686, Japan  \n319 Eindhoven University of Technology, 5600 MB Eindhoven, Netherlands  \n- 320 Department of Physics and Astronomy, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 143-747, Republic of Korea 321 Concordia University Wisconsin, Mequon, WI 53097, USA  \n322 School of Physics and Technology, Wuhan University, Bayi Road 299, Wuchang District, Wuhan, Hubei, 430072, China", 'ABSTRACT': 'We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope ( Swift -BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum-likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift -BAT false alarm rate is less than 10 -3 Hz, we compute the GW-BAT joint false alarm rate. Finally, the derived Swift -BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers.', '1. INTRODUCTION': 'The discovery of gravitational waves (GWs) from coalescing binary black holes (BBH) by the Laser Interferometer Gravitational-Wave Observatory (LIGO) opened  \na new window to the Universe (Abbott et al. 2016a). In addition to GWs, compact binary mergers with at least one neutron star (NS) component are likely to generate electromagnetic (EM) radiation (e.g., Nakar 2020a; Kyutoku et al. 2021). Coincident detection of EM emission from compact binary mergers provides a complete picture of the merger process and can have huge implications for our understanding of the Universe. Such coincidences play a crucial role in tracing the properties of the source host galaxy (Troja et al. 2017; Alexander et al. 2017), mitigating degeneracies in GW parameter estimation (Abbott et al. 2017a; Hughes & Holz 2003; Wang & Giannios 2021), placing constraints on the NS equation of state (Bauswein et al. 2017; Radice et al. 2018), and investigating the expansion rate of the Universe, thereby testing cosmological models (Abbott et al. 2017a,b; Hotokezaka et al. 2019; Nissanke et al. 2013; Schutz 1986). Additionally, they allow for the measurement of arrival time differences between photons and gravitons, providing limits to the mass of a graviton, exploring potential violations to the equivalence principle and Lorentz invariance (Abbott et al. 2017e).  \nThe joint detection of the first GW event consistent with a binary NS (BNS) coalescence GW170817 (Abbott et al. 2017a), and a coincident short gamma-ray burst GRB 170817A (Goldstein et al. 2017a; Savchenko et al. 2017), accompanied by the optical/infrared kilonova counterpart AT 2017gfo (Arcavi et al. 2017; Coulter et al. 2017; Tanvir et al. 2017; Evans et al. 2017; Pian et al. 2017; Smartt et al. 2017; Drout et al. 2017; Cowperthwaite et al. 2017) and the GRB afterglow (in the X-rays: Troja et al. 2017; Margutti et al. 2017 and radio: Hallinan et al. 2017), together ushered in a new era in the field of multi-messenger astrophysics and forever impacted our comprehension of compact binary coalescences (CBC) involving an EM counterpart. Massive coordinated EM follow-up efforts were dedicated to deeply monitor the error regions derived from the joint sky localization of GW detectors and high-energy satellites, helping to reduce the initial three detector network sky localization from 28 deg 2 to within a few arcseconds of the host galaxy NGC 4993 (Abbott et al. 2017d,a). The spectacular spectral and light curve evolution of this transient (Abbott et al. 2017d; Villar et al. 2017) suggested that this explosive event was an active site for r-process nucleosynthesis (Pian et al. 2017; Smartt et al. 2017; Coulter et al. 2017; Drout et al. 2017) (for a detailed review of the multimessenger observations of GW170817, see, e.g., Nakar 2020b; Margutti & Chornock 2021).  \nThe expected EM counterpart emission from BNS or neutron star-black hole (NSBH) mergers can poten-  \ntially be weak due to various factors such as considerable source distances, an off-axis viewing angle, or limited amount of ejected mass. For the specific case of GW170817, despite a coincident GRB detection, it took nearly half a day to localize the host galaxy and begin observations of the kilonova (Abbott et al. 2017a). Prompt targeted searches around the GW trigger times, leveraging facilities with enhanced localization capabilities, can refine search strategies and assist optical or infrared (IR) facilities in correctly identifying and pursuing transient candidates for subsequent follow-up studies. In addition to prompt searches, Fermi -GBM analysis of triggers from the first and second LIGO-Virgo observing runs showed that targeted offline searches are capable of recovering additional candidate joint events that may be of astrophysical relevance (Hamburg et al. 2020; Pillas et al. 2023). Temporal and spatial coincidence information can be used to derive the joint false alarm rate (FAR). These estimates have the potential to elevate subthreshold triggers in either the GW or GRB domains to the status of an above-threshold candidate detection (Nitz et al. 2019).  \nUnlike Fermi , Swift has been for a long time incapable of relaying a continuous stream of event mode data to the ground in real time. Such a capability was enabled through GUANO (Gamma-ray Urgent Archiver for Novel Opportunities, described in Section 2) (Tohuvavohu et al. 2020), which recovers event data from the Swift Burst Alert Telescope (BAT, Barthelmy et al. 2005), that then get processed by the Non-Imaging Transient Reconstruction And TEmporal Search (NITRATES, DeLaunay & Tohuvavohu 2022) pipeline (see Section 4) to search for subthreshold transient candidates. 1 In addition to other astronomical transients, such as GRBs, fast radio bursts (FRBs), and highenergy neutrinos, the GUANO-NITRATES infrastructure performs targeted searches on GW events communicated by the LVK Collaboration, to detect possible GRBs associated with CBCs.  \nThe impact and potential of Swift -BAT subthreshold searches are crucial for multi-messenger related goals. Indeed, deeper targeted searches increase the joint detection horizon, thus enhancing the probability of finding weak EM counterparts of CBCs in the hard X-ray domain. Moreover, thanks to the high spatial accuracy enabled by the BAT coded mask, subthreshold searches open the possibility of recovering the position of the  \ncandidate EM event at the precision level of a few arcminutes, fundamental to drive the subsequent follow-up with ground and space-based EM facilities.  \nCurrently, the targeted search analysis carried out thanks to GUANO, has enabled the discovery of more than 35 GRBs with arcminute localization. A total of 7 of the detected GRBs have a duration < 2 s (e.g., DeLaunay et al. 2020; Tohuvavohu et al. 2022a), hence they are potentially associated with CBCs containing at least one NS. GUANO data have also been used for the localization of 29 long GRBs through imaging (e.g., DeLaunay et al. 2021a) and non-imaging analysis techniques (e.g., DeLaunay et al. 2021b; Tohuvavohu et al. 2022b). GRB 220107A, detected during BAT slew and localized with arcminute precision, enabled the first optical redshift measured using GUANO data (DeLaunay et al. 2022a). The arcminute localization of GRB 211106A enabled prompt multiband follow up and led to the discovery of the first afterglow in the millimeter band from a short GRB (Tohuvavohu et al. 2021a). With GUANO, one can additionally recover coarse localization information on GRB-like transients that originate from outside the BAT field of view (FOV; e.g., DeLaunay et al. 2023).  \nIn addition to the application to real-time analysis, the availability of BAT data enables us to perform a systematic, deeper targeted search focused on archival LVK triggers. The goal of this study is to perform such an analysis on all the LVK triggers received during the third LIGO-Virgo observing run, during which the GUANO pipeline started to be fully operational. The run duration was comprised of two segments: O3a, which operated from April 1, 2019, 15:00 UTC to October 1, 2019, 15:00, and O3b which operated from November 1, 2019, 15:00 UTC, to March 27, 2020, 17:00 UTC. The alerts distributed during O3 were reporting the following parameters: FAR, the signal classification (CBC or unmodeled Burst), and the associated astrophysical probabilities. The results of O3 are summarized in Gravitational-Wave Transient Catalog data releases GWTC-2 (Abbott et al. 2021), GWTC-2.1 (Abbott et al. 2024), and GWTC-3 (Abbott et al. 2023).  \nIn this work, we use Swift -BAT observations to carry out offline targeted subthreshold searches for EM counterparts of the GW triggers obtained during O3. The rest of the paper is organized as follows: In Section 2, we describe the Swift -BAT instrument and its new capabilities, and in Section 3, we provide details about the GW trigger sample used for the analysis. In Section 4 we summarize the targeted search method adopted for the analysis. We present the results from our targeted search analysis on the various subcategories of triggers  \nin Section 5, and discuss the scientific interpretation in Section 6.', '2. Swift -BAT': 'The Neil Gehrels Swift Observatory (henceforth, Swift ) is a GRB-focused mission launched in 2004, with three onboard payloads - the Burst Alert Telescope (BAT), the X-ray Telescope (XRT), and the UltraViolet/Optical Telescope (UVOT) - covering the EM spectrum from hard X-rays and gamma-rays all the way to the optical (Gehrels et al. 2004). The BAT instrument (Barthelmy et al. 2005) is a hard X-ray coded mask imager with a wide FOV that operates in the broad energy band of 15-350 keV. It is the primary instrument that detects GRBs and performs an onboard imaging analysis via a cross-correlation between the spatial pattern of the counts across the detector array and the pattern of the coded mask. The sensitivity of BAT is capable of providing arcminute localizations of GRBs triggered onboard (Gehrels et al. 2004). Due to the lack of continuous downlinking of timing, spatial, and energy information for each detector count (event mode data), carrying out targeted searches offline has not been possible in the past. A new infrastructure, called GUANO, was incorporated into the Swift -BAT operations in 2019. Details of the GUANO operations can be found in Tohuvavohu et al. (2020). From the outset, GUANO has demonstrated that recovering event mode data from astrophysically compelling time windows can enhance the overall transient detection rate and sensitivity of BAT (Tohuvavohu et al. 2020).', '3. GRAVITATIONAL-WAVE TRIGGER SAMPLE': "This paper focuses on the Swift -BAT subthreshold analysis of a sample of GW triggers with a FAR < 2 per day, distributed by the LVK Collaboration during O3. The subthreshold GW alerts were received by the EM follow-up groups that were part of a Memorandum of Understanding with the LVK Collaboration. For candidates found with CBC search pipelines (Dal Canton et al. 2021; Sachdev et al. 2019; Messick et al. 2017; Aubin et al. 2021; Nitz et al. 2018; Hooper et al. 2012) and Burst search pipelines (Klimenko et al. 2005, 2016), the alerts contain basic information about the GW FAR, the probability of the candidate being astrophysical ( p astro ) and trigger time. In the case of CBC candidates, the alerts received in low latency report the p astro split in the four CBC classes: BBH, BNS, NSBH, and Mass Gap. The Mass Gap category includes CBC candidates in which at least one component has a mass in the range [3-5] M ⊙ . Using the GW trigger information received in low latency, we can further perform a search for associations in BAT data.  \nFrom the list of 1552 alerts that were communicated via low latency channels, we obtained successful GUANO data dumps for 636 triggers. The GW information of the candidates received in low latency are reported in Table 1. The FAR and the p astro classification reported here correspond to the preferred event, namely the one with the highest SNR. Swift -BAT event mode data coincident with the GW trigger time were made available for these triggers in real time via the GUANO data dumps. Post-processing on the data was carried out on this sample from O3 using the NITRATES analysis pipeline (see Section 4).  \nOut of the 636 low-latency alerts, a total of 86 GW candidates have been confirmed by the offline analysis and included in the GWTC-2.1 and GWTC-3 data releases (Abbott et al. 2024; Abbott et al. 2023). Among the 86 confirmed candidates, 14 triggers have p astro > 0 . 5 and 72 triggers have p astro < 0 . 5. We indicate the details of the confirmed candidates with p astro > 0 . 5 and p astro < 0 . 5 in Table 2 and Table 3, respectively. The values of FAR, p astro and CBC Class given in Table 2 and Table 3 are derived from offline analyses, as reported in GWTC-2.1 and GWTC-3 data releases, hence are considered more reliable than the values obtained in low latency, reported in Table 1. The FAR and the p astro classification reported in Tables 2 and 3 come from the pipeline that gives the highest value of p astro . For high significance events, if multiple pipelines derive a p astro ≃ 1, we select the one with highest SNR. According to these rules, in the case of S200225q, reported in Table 2, the selected pipeline is cWB, but the event is classified as CBC. The CBC class is determined by the highest among p BBH , p NSBH and p BNS . In Tables 2 and 3 we report the value of p Class defined as max[ p BBH , p NSBH , p BNS ]. It is possible that some candidates marked as 'Burst' in Table 1 are then re-classified as 'CBC' by the offline analysis. Therefore, for each catalog event the most updated group is the one reported in Tables 2 and 3.  \nIn Figure 1 we show the histograms of the p astro probabilities for all the low-latency CBC candidates processed by GUANO, for a total of 424 candidates, divided into 67 BBH, 130 BNS, 148 NSBH, and 79 Mass Gap events (Fig. 2, left panel). In the offline postprocessing of GW candidates, the Mass Gap classification was removed, classifying an object with M > 3 M ⊙ as a black hole. This implies that all the CBC events with at least one component mass in the range [3 -5] M ⊙ are re-distributed into the BBH and NSBH classes. The distribution of the updated p astro classification released by LVK is over-plotted in Figure 1, and the classes are  \ndivided in 39 BBH, 22 BNS and 17 NSBH candidates (Fig. 2, right panel).", '3.1. GW sky localization': "For the selection of the GW sky localization for each candidate we adopt the following scheme:  \n- 1. Above-threshold GW candidate: The GW candidate is contained in the list of high-probability ( p astro > 0 . 5) candidates reported in Table 2 of GWTC-2.1 (Abbott et al. 2024) or Table 1 of GWTC-3 (Abbott et al. 2023). The GW sky localizations are downloaded from the parameter estimation data releases of GWTC-2.1 and GWTC3. 2 We use the results derived from a combination of IMRPhenomXPHM (Pratten et al. 2021) and SEOBNRv4PHM (Ossokine et al. 2020) waveforms (labeled as 'Mixed' in the release).\n- 2. Subthreshold GW candidate: The GW candidate is classified as low-probability ( p astro < 0 . 5) in the offline analysis. The GW sky localization is produced by BAYESTAR (Singer & Price 2016; Singer et al. 2016) and is taken from the GWTC-3 release, which contains both O3b events and updated O3a events. If multiple events are present for a single GW candidate, the pipeline with the highest p astro is considered for the selection of the sky localization.\n- 3. Non-confirmed low-latency GW candidate: The GWcandidate has an associated low-latency alert, but the event has not been confirmed by the offline analysis. The GW sky localization is downloaded from GraceDB, selecting the preferred event.", '4. TARGETED SEARCHES USING GUANO-NITRATES': 'Targeted searches are carried out in real time for all types of transients such as GRBs, FRBs, neutrino events as well as GW triggers, on the event mode data obtained using GUANO. The targeted search pipeline that is currently operational is NITRATES. This is a maximum-likelihood framework that forward models signals through the entire instrument response (DeLaunay & Tohuvavohu 2022). The BAT responses are created by simulating the photon paths through all detector segments using Geant4, which is a particle-interaction simulator software toolkit (Allison et al. 2016). Unlike the standard BAT responses, the NITRATES responses  \n40  \n35  \nReceived in low-latency  \nConfirmed by offline analysis  \n<!-- image -->  \n<!-- image -->  \n30  \n1  \n.  \n5  \nlog10[P(BBH)]  \n<!-- image -->  \n<!-- image -->  \nFigure 1. Distribution of the p astro values for the CBC triggers detected during O3, with available GUANO data dumps. We distinguish with different colors the triggers received in low latency and the ones confirmed by offline analysis.  \n<!-- image -->  \nFigure 2. Left: distribution of the CBC triggers from O3 received in low latency, which had successful GUANO data dumps, divided in the BBH, NSBH, BNS, and Mass Gap classes. Right: analogous distribution for the CBC candidates confirmed by the offline analysis. In the post-processing, the Mass Gap classification has been subsumed into BBH and NSBH.  \n<!-- image -->  \n-  \n1  \n.  \n0  \n<!-- image -->  \naccount for all the detectors on the focal plane, regardless of their coding by the mask. The responses also encode details on the gamma-ray interactions inside the instrument, which carry additional information. This approach enables substantial sensitivity gain compared to the conventional technique of cross-correlation imaging, which translates to a 50% increase in the detection horizon distance for a GRB 170817A-like burst compared to the onboard imaging. Details of the NITRATES response generation and the analysis pipeline are provided in DeLaunay & Tohuvavohu (2022).  \nA GRB-like transient signal is described using the sky localization and parameters that are specific to the assumed spectral model (the peak energy and spectral slope). This framework then computes the significance of each signal using a test statistic (TS) by maximizing the log-likelihood (LLH) as a function of signal parameters. The likelihood ratio test statistic Λ is used to compare the source signal+background model (described using a set of parameters, Θ sig that maximizes the LLH) to a background-only model (described by the set of parameters Θ off bkg , that maximizes the LLH in the off-time window) and is defined as follows (DeLaunay & Tohuvavohu 2022):  \nΛ = -2[LLH(Θ off bkg | N on ) -LLH(Θ sig , Θ off bkg | N on )] , (1)  \nwhere N on is on-time data.  \nThe search pipeline workflow can be summarized as follows:  \n- 1. The event mode data is cleaned and filtered to discard potential glitches and artifacts from cosmic rays, and to flag poorly behaving detectors. Good time intervals (GTIs) are determined, where there is quality data for the analysis.\n- 2. A time window of 50 s (from the pre- and posttrigger intervals) is identified as the background interval. It is then utilized to model contributions to the background from known bright sources and diffuse sources.\n- 3. To narrow down the search parameter space, a set of simple analyses are performed to select a list of interesting start times and durations (hereafter referred to as time seeds) as well as portions of the BAT FOV (position seeds).\n- 4. Finally, the log-likelihoods are computed for all parameters corresponding to the shortlisted time and position seeds.  \nIn essence, the set of signal parameters that maximizes the log-likelihood is the most preferred set of parameters.  \nThe NITRATES likelihood analysis outperforms the onboard mask-weighted imaging analysis by delivering superior sensitivity, given the increased effective area (see Fig. 2 in DeLaunay & Tohuvavohu 2022). At the cost of a significantly increased computational time, this method is capable of delivering arcminute scale localization for events that fall inside the BAT FOV, even when the transient event does not trigger Swift -BAT onboard (Tohuvavohu et al. 2021b; Tohuvavohu 2023; DeLaunay et al. 2022b). The NITRATES pipeline has the ability to distinguish between bursts that come from in and outside the BAT FOV. NITRATES has also accurately localized sufficiently bright bursts outside the FOV (DeLaunay & Tohuvavohu 2022).  \nSwift -BAT GUANO was operating during the O3 and was successfully procuring event mode information in response to GW subthreshold triggers (Tohuvavohu et al. 2020). We describe the targeted search analysis that has been carried out using the NITRATES version 0.0.1 which was available in early 2022. 3 The targeted search analysis that was operational in O3 corresponded to a preliminary version of the NITRATES code, that has since undergone several stages of development. The most updated version is publicly available on GitHub. 4  \nDuring O3, for a total of 636 GW triggers, GUANO dumped either 200 s or 90 s of event mode data, for public triggers and for privately communicated triggers, respectively. The choice of the width of the temporal window is made to avoid an overload of downlink data in the process of GUANO data dump. The targeted search pipeline was run in a time window of ± 20 s centered around the trigger time. The search was carried out on 8 time bins (0.128 s, 0.256 s, 0.512 s, 1.024 s, 2.048 s, 4.096 s, 8.192 s and 16.384 s) and 9 energy bins (between 15-350 keV). The results from the search are reported using the following set of parameters: 1) the maximum √ TS describes the statistical significance of a potential detection (see Section 5); 2) ∆LLH out indicates the preference of the search to a location inside or outside the BAT FOV, and 3) ∆LLH peak indicates the confidence of the search in localizing the source to arcminute scales.  \nThe NITRATES search was performed on the ROAR supercomputing cluster on a set of 200 virtual cores for a total of ∼ 600 × 800 CPU hours for the entire GW sample.', '5. RESULTS FROM NITRATES': 'The targeted search analysis provides a list of top candidates whose spatial, temporal, and spectral parameters maximize the log-likelihood. In order for a candidate to be qualified as a confident detection, we require that the resulting detection significance parameter √ TS must exceed the threshold value of 8, corresponding to a FAR ∼ 4 × 10 -5 Hz. Being a targeted search, the NITRATES analysis can give a false positive with √ TS > 8 with a probability which follows a Poissionian distribution:  \nP ( N det ≥ 1) = 1 -P ( N det = 0) = 1 -e -FAR × ∆ t , (2)  \nwith ∆ t = 40 s being the width of the search window. This leads to a pre-trial p-value of 1 . 6 × 10 -3 . Since the NITRATES analysis is performed on all GW triggers with a FAR < 2 day -1 , and considering that there are N GW -search = 5 independent GW pipelines, the rate of expected false positive candidates with √ TS > 8 is ∼ 5 × 2 × 1 . 6 × 10 -3 /day ∼ 1/(60 day). 5  \nFor the entire sample of 636 low-latency triggers processed using NITRATES, we have no candidates that qualify as detection of a signal of astrophysical origin. None of the top candidates within the ± 20 s search window are coincident with the GW triggers. A temporal coincidence with a GW trigger is claimed if the NITRATES search finds a candidate with √ TS > 8 and | t 0 -t start | < 20 s, where t 0 is the GW trigger time and t start is the starting time of the temporal bin with highest ranking statistics. A detailed list of all the NITRATES results for the entire sample analyzed during O3 is provided in Table 1. We discuss specific false positive candidates in Section 5.1.  \nIf the GW trigger time is included in the time window corresponding to slew mode of BAT, the analysis cannot be performed using NITRATES since the targeted search requires stable attitude information to compute the background. Similarly, some triggers have insufficient exposure time, preventing the NITRATES analysis. In this case neither TS results nor flux upper limits can be computed. As a cut to narrow down the parameter space, the targeted search selects time seeds as described in Section 4. If there are no time seeds that pass the preliminary cuts then there will be no final likelihood computations. Results for these types of triggers are indicated as NFL (No Final Likelihood) in Table 1. In the case of NFL triggers, though, the flux upper limit can be computed, since a full likelihood analysis is not required.', '5.1. False positives': 'We did not find any candidate associations from any of the triggers with BAT. However, the targeted search pipeline did result in the detection of six candidates with a significance above the NITRATES detection threshold of √ TS = 8. These candidates were examined to understand our false positive population. S200327j ( √ TS ∼ 22), S200324ax ( √ TS ∼ 11), and S200225af ( √ TS ∼ 10 . 5) are triggers that occurred during the passage of Swift in the proximity of the South Atlantic Anomaly (SAA). The background characterization becomes unreliable when the spacecraft is either entering or leaving the SAA on account of increased background contamination. In S190919au, a peculiar dip ( ∼ 20 s) in the background may have contributed to a false elevation in the signal detection statistic, by causing an under representation of the background rate. For S190919u, we obtain a √ TS ∼ 8 . 0, which corresponds to a FAR of ∼ 4 × 10 -5 Hz. The presence of such a detection, in a total sample of 636 triggers that were analyzed, is compatible with the expected number of false positives, which corresponds to (4 × 10 -5 Hz) × (40 s) × 636 ∼ 1.  \nS200130ai corresponds to a subthreshold GW trigger at T 0 = 2020-01-30T09:59:58 that was identified by the CBC search as a NSBH candidate with a p astro = 0 . 008 and a GW FAR ∼ 1 . 8 × 10 -5 Hz. It was detected using NITRATES at a significance of √ TS ∼ 16 . 3 with a ∆LLH out = -19 . 68 and ∆LLH peak = 2 . 14, consistent with a sky localization outside the BAT-FOV. The highest log-likelihood candidate, was identified to arise 1.5 s prior to the GW trigger time. Due to the low value of ∆LLH peak , we do not have an arcminute-level precision on the sky localization. The candidate was associated with a Fermi trigger 602071201 (GCN 26944, Fermi GBM Team 2020) and was classified as a long GRB. The Fermi localization is RA = 137 . 5 deg, Dec = -51 . 3 deg, with a statistical uncertainty of 3.5 degrees. The Interplanetary Gamma-Ray Burst Timing Network (IPN) further localized the event in a 3-sigma error box with an area of 1487 arcmin 2 and centered at an RA = 134 . 742 deg and Dec = -49 . 627 deg (Hurley et al. 2020). Although this event presents a temporal coincidence with the GW trigger, on account of the lack of spatial coincidence with the GW location, this event is discarded from being associated with the GW subthreshold trigger. Additionally, this low-latency GW candidate has not been confirmed by offline analyses.', '5.2. Computation of flux upper limits': 'Since each GW trigger processed in this analysis resulted in a non-detection in Swift -BAT, we estimate the flux upper limits in the following manner. The  \nFigure 3. Flux upper limit maps are shown for all the O3 catalog events with a p astro > 0 . 5 that were processed successfully using NITRATES. The color bar indicates the upper limit in the 15-350 keV Swift -BAT band as a function of the sky position. The part of the sky in white corresponds to the area covered by the Earth. The solid and dashed contours are the GW 90% and 50% credible levels, respectively.  \n<!-- image -->  \nNITRATES analysis generates rates curves in the 15350 keV energy band from the GTIs of the filtered event list. The number of active detectors corresponding to each trigger is read out from its respective detector mask file. A linear fit is carried out to the background window of duration 50 s. We then estimate the 5 σ count rate and the corresponding uncertainty over the full signal window which has a ± 20 s duration. This is computed for all the 8 time bins (see Section 4). We further convert the 5 σ count rates to flux upper limits, as a function of sky position, in the 15-350 keV band, by convolving different spectral models with the NITRATES responses for each time bin iteration. We select 929 grid points on the sky and interpolate upper limit values for locations in between. We assume the following different spectral templates:  \n- 1. Band function (Band et al. 1993) with a soft template ( E peak = 70 keV, α = -1 . 9, β = -3 . 7)\n- 2. Band function with a normal template ( E peak = 230 keV, α = -1.0, β = -2.3)\n- 3. Cutoff power law function with a hard template ( E peak = 1500 keV, α = 1.5)\n- 4. Cutoff power law function that has been used to describe GRB 170817A ( E peak = 185 keV, α = 0.62) (Goldstein et al. 2017b)  \nThe parameters α and β correspond to the low-energy and high-energy photon indices of the spectrum, respectively. The first three spectral templates are identical to the ones that are routinely adopted by Fermi -GBM (Goldstein et al. 2016a). In the rest of the paper, all the  \nFigure 3. (continued)  \n<!-- image -->  \nresults are reported assuming a Normal spectral template.  \nCalling Ω = (RA , Dec) the coordinates variable, for each temporal bin and spectral template we convert the upper limit map ϕ UL (Ω) into a unique marginalized upper limit value:  \nϕ UL = ∫ Ω / ∈ Ω ⊕ ϕ UL (Ω) P GW (Ω)dΩ , (3)  \nwhere P GW (Ω) is the posterior probability distribution of the GW sky position. The notation Ω / ∈ Ω ⊕ means that the integral is limited to the region of the sky not occulted by the Earth. We report in Table 1 the marginalized flux upper limits for a 1 s time bin, assuming the normal spectral template. In Fig. 3, we provide the sky maps reporting both the flux upper limits as a function of sky position and the GW contours (50% and 90% credible levels) for the GW candidates with p astro > 0 . 5.  \nAs additional information, we also report the quantity ε in BAT , which quantifies the probability that the GW source is inside the BAT coded FOV and corresponds to  \nε in BAT = ∫ Ω ∈ Ω in P GW (Ω)dΩ , (4)  \nwhere the integral is limited to the solid angle Ω in , namely the portion of the sky where the BAT partial coding fraction is larger than 0.01. The location of Ω in , i.e., the BAT FOV, is identified by the yellow region in the sky maps of Figure 3. The higher the ε in BAT , the better the BAT covering of the GW error region, and the more constraining the derived upper limit. The flux upper limits as a function of ε in BAT for all GW trigger candidates is shown in Fig. 4. We also indicate with different markers the sample of low-latency triggers, the confirmed list of subthreshold candidates ( p astro < 0 . 5), and the above-threshold candidates ( p astro > 0 . 5). In Table 1 we also report the probability that the GW  \n⊕  \n0 . 0 0 . 2 0 . 4 0 . 6 0 . 8 1 . 0 ε in BAT Figure 4. The 15-350 keV flux upper limit ϕ UL derived with NITRATES as a function of ε in BAT , namely the probability that the GW candidate is contained inside the BAT FOV. The plot includes all the GW candidates received during O3, including both Burst and CBC events. With different symbols we distinguish the GW candidates received in low latency and the ones confirmed and included in the O3 catalog, separated in p astro > 0 . 5 and p astro < 0 . 5. The dashed and solid red lines are the 50% and 90% containment regions of the scatter plot, respectively. The one-dimensional histograms of ϕ UL and ε in BAT are reported on the sides. The color bar indicates the value of ε ⊕ , the probability that the GW candidate is occulted by the Earth.  \n<!-- image -->  \nsource is occulted by the Earth, defined as:  \nε ⊕ = ∫ Ω ∈ Ω ⊕ P GW (Ω)dΩ , (5)  \nwhere Ω ⊕ is the solid angle subtended by the Earth from the Swift reference system.', '5.3. Computation of luminosity upper limits': 'We further convert the flux upper limits into luminosity upper limits for all the GW triggers with available information about the distance posterior distribution, namely only triggers identified by CBC searches. The luminosity upper limit in the rest frame band 1 keV10 MeV is estimated as  \nL UL = ⟨ 4 πD 2 L kϕ UL ⟩ , (6)  \nwhere D L is extracted from the posterior probability P ( D L ) reported in the GW sky localization files, while k is the k-correction and corresponds to  \nk = I [1 keV / (1 + z ) , 10 MeV / (1 + z )] I [15 keV , 350 keV] , (7)  \nwhere  \nI [ a, b ] = ∫ b a E d N d E ( E )d E, (8)  \nand d N /d E is the assumed photon spectrum. The band 1 keV-10 MeV is chosen since it is usually adopted to report the bolometric luminosity of GRBs.  \nThe luminosity upper limits as a function of the mean value of the luminosity distance is reported in Figure 5. Similar to what was shown previously, we demarcate the various samples. Candidates with a low latency classification of Mass Gap were later re-distributed to other categories as part of post-processing, which is evident from the Mass Gap panel in Figure 5. As expected and as evident already from Figure 4, we see a clear correlation between the luminosity upper limit and ε in BAT in Figure 5, indicating that the inferred constraints on the EM counterpart are more stringent when the GW probability integrated inside the BAT FOV is higher. Since ϕ UL is an upper limit and not a measure coming from a detection, Eq. (6) is an approximated method to convert ϕ UL in a luminosity upper limit, averaging over the P ( D L ) distribution provided by the GW analysis. In Appendix A we show a more accurate way to estimate the luminosity upper limit, but we find no relevant differences with respect to the method reported in this section. The Eq. (6) is used only to produce the plots of Fig. 5, but this approximation is not used  \nin Section 6 to perform inference about the EM model parameters. Instead, in Section 6 a reverse process is followed, namely the EM model is used to predict the luminosity, which is then convolved with P ( D L ) to obtain a probability distribution of the flux in the BAT energy band.', '5.4. Computation of joint FAR': 'To calculate the joint GW-BAT FARs for each GW trigger, we elaborate on the methods used to compute the individual FARs and subsequently combine them. To derive the sensitivity of the NITRATES search, timetagged event data assembled from intervals corresponding to calibration runs and data from before and after known GRB signal times (total exposure time of ∼ 51 ks) were analyzed. The behavior of the background population and its associated FAR were then identified (see Section 7.6 and Fig. 16 in DeLaunay & Tohuvavohu 2022). We further compute the joint Swift -BAT-GW FAR by combining the BAT FAR (calculated using the method described above) with the GW FAR. A targeted joint FAR threshold routine is constructed as part of the Rapid, on-source VOEvent Coincident Monitor (RAVEN; Abbott et al. 2017c; Urban 2016), which combines the FARs obtained from GWs along with those from BAT and computes the joint temporal as well as the joint spatial FAR. We also specify details of the search pipeline used in the process, Burst or CBC. The joint FAR prescription as reported in the RAVEN documentation, 6 is computed as  \nFAR GRB+GW = Z I Ω [ 1 + ln( Z max Z ) ] (9)  \nwhere Z is the joint ranking statistic given by,  \nZ = FAR GW FAR GRB ∆ t, (10)  \nZ max = FAR GW , max FAR GRB , max ∆ t, (11)  \nand we adopt ∆ t = 30 s, FAR GW , max = 2 day -1 and FAR GRB , max = 10 -3 Hz. I Ω is an integral that quantifies the spatial overlap between the GW localization and the GRB localization (Ashton et al. 2018). Even if the search of subthreshold candidates in NITRATES is done in a temporal window [ t 0 -20 s , t 0 + 20 s] around the trigger time t 0 , for the RAVEN joint alert the adopted temporal window is [ t 0 -10 s , t 0 + 20 s]. Since none of the BAT candidates analyzed in this work has a confident estimation of the sky localization, we adopt a uniform posterior probability on the full sky  \nfor the EM candidate. Hence, by definition, we set I Ω = 1. The candidate triggers a RAVEN alert when the FAR GRB+GW × N t < FAR max , with N t being the trials factor of the joint search and FAR max = (1/30) day -1 for CBC events and FAR max = 1 yr -1 for Burst events. The trials factor corresponds to N t = S GW ( S GW -1), where S GW is the number of search GW pipelines, 4 for CBC events and 3 for Burst events. Since the GW pipelines are not fully independent, a realistic value of the trials factor is smaller than the one adopted here, therefore the RAVEN threshold can be considered as a conservative estimate for the significance of a joint detection.  \nWe quote the derived joint FARs along with other trigger-specific details only for those triggers with a FAR GRB , max < 10 -3 Hz in Table 4. We find that, after rejecting false positives, 2 CBC events pass the joint FAR detection threshold to trigger a RAVEN alert. Specifically, S191110x ( √ TS = 7 . 2) and S200108p ( √ TS = 7 . 4) have a joint FAR of 3 . 02 × 10 -4 yr -1 and 21 . 3 yr -1 , respectively. These values are obtained considering the GW FAR received with the low-latency alert. In the offline analysis of the GW candidates, neither S191110x or S200108p have been confirmed. We therefore conclude that, considering the offline joint analysis of GW and Swift -BAT data, none of the candidates is eligible to claim a significant joint detection.  \nIn Fig. 6 we report the location in the GW FAR -√ TS plane of all the candidates that pass the condition FAR GW , max < 2 day -1 and FAR GRB , max < 10 -3 Hz (i.e., √ TS ≳ 7), to be considered for a potential joint alert. The astrophysical origin of all the candidates with √ TS > 8 has been rejected as discussed in Section 5.1, and therefore they are not reported in Fig. 6. The dashed black and red lines mark the separation line for the event to pass the RAVEN alert threshold, for CBC and Burst candidates, respectively. Candidates below those lines would have triggered a RAVEN alert.', '6. SCIENCE DISCUSSION': 'In this section, we describe how the upper limits derived from the joint subthreshold search can be used to infer constraints about possible EM emission from the GWcandidates. Starting from a model of the EM emission, the luminosity in the BAT band can be estimated, whose value will depend on some internal parameters of the model ( λ 1 , ..., λ k ). The goal is to explore the model parameter space and test if the estimated flux is in agreement with the upper limit constraints derived in this paper.  \nFor this purpose, a knowledge of the distance of the GWcandidate is needed, and the GW sky localization is  \n)  \n1  \n-  \ns  \n(erg  \nUL  \nL  \n10  \n10  \n10  \n10  \n2  \nFigure 5. Upper limits on the luminosity computed in the rest frame 1 keV-10 MeV energy band, as a function of the mean luminosity distance extracted from the sky localization map of each GW candidate. The color bar indicates the quantity ε in BAT , namely the probability that the GW candidate is located inside the BAT coded FOV. With different symbols, we distinguish the GW candidates received in low latency and the ones confirmed and included in the O3 catalog, separated in p astro > 0 . 5 and p astro < 0 . 5.  \n<!-- image -->  \n<!-- image -->  \nMass Gap  \n- Low-latency events\n- Catalog events with p astro < 0 . 5\n- Catalog events with p astro > 0 . 5  \nDL  \n(Mpc)  \n<!-- image -->  \n10  \n<!-- image -->  \nBNS  \nused to extract the posterior distribution P ( D L ). Since only CBC events have such information, Burst events are not considered in this discussion. For the CBC events, we consider a phenomenological model which describes the probability distribution of the luminosity L (in the 15-350 keV rest-frame)  \nP ( L ) = (1 -f ) δ ( L = 0) + f Π( L ) . (12)  \nHere, the f parameter is a proxy for the EM-bright nature of the event, i.e., given a CBC source described by a set of GW parameters ⃗ θ GW , f ( ⃗ θ GW ) corresponds to the probability that the EM luminosity of the source is nonzero. On the other hand, Π( L ) is the intrinsic luminosity function of the EM transient associated with the specific CBC class. In the case of BNS and NSBH candidates,  \nthe assumption on Π( L ) should be informed by our prior knowledge of the luminosity function of merger-driven GRBs. A detailed study of the impact of this work on our knowledge of merger-driven GRBs luminosity function will be reported in a follow-up paper. In this section, instead, we focus only on the BBH class, for which no strong prior exists for Π( L ). For simplicity and in order to show the constraining power of our joint subthreshold search, we assume that the EM process associated with BBH, if present, produces a universal, viewing angle-independent luminosity L 0 . Therefore, in the scenario specified above, we have ( λ 1 , ..., λ k ) = ( f, L 0 ) and  \nP ( L ) = (1 -f ) δ ( L = 0) + fδ ( L -L 0 ) = P ( L ; f, L 0 ) . (13)  \n49  \n48  \n47  \n3  \n0  \n.  \n6  \n0  \n.  \n4  \n0  \n.  \n2  \n0  \n.  \n0  \nT  \nBA  \nin  \nε  \nFigure 6. Distribution in the GW FAR-√ TS plane of all the triggers which passed the threshold FAR GRB < 10 -3 Hz. Triggers in the regions below the black and red dashed lines (marked with a green cross) would have triggered the RAVEN alert system, for CBC and Burst events, respectively. The plot does not include all the triggers that have a √ TS > 8 which turned out to be spurious artifacts.  \n<!-- image -->  \nOnce the model for the EM emission is specified, the probability distribution of the predicted flux is  \nP ( ϕ ) = (1 -f ) δ ( ϕ = 0) + fP EM ( ϕ ) , (14)  \nwhere P EM ( ϕ ) = P ( L 0 / 4 πkD 2 L ) is the flux probability distribution in the assumption that the source is EM bright. Hence, for the i -th candidate, the probability that the predicted flux is below the estimated upper limit ϕ 0 ,i corresponds to  \nP i ( ϕ < ϕ 0 ,i ) = (1 -f ) + f ∫ ϕ 0 ,i 0 P i ( ϕ )d ϕ, (15)  \nvalid in the limit in which the GW candidate is assumed to be real. Therefore, given a candidate GW with a probability of being astrophysical p astro ,i = π i , there are only three possibilities to have a non-detection in BAT:  \n- 1. The source is not astrophysical, with a probability 1 -π i ;\n- 2. The source is astrophysical, but it is occulted by the Earth, with a probability π i ε ⊕ ;\n- 3. The source is astrophysical, it is not occulted by the Earth and the predicted flux by the EM model is below the BAT upper limit, with a probability π i (1 -ε ⊕ ) P i ( ϕ < ϕ 0 ,i ).  \nThis allows us to define a non-detection likelihood corresponding to  \nL i = (1 -π i ) + π i [ ε ⊕ +(1 -ε ⊕ ) P i ( ϕ < ϕ 0 ,i )] . (16)  \nIn the case of L 0 → 0, P i ( ϕ < ϕ 0 ,i ) → 1, so L i → 1. For very large values of L 0 , instead, P i ( ϕ < ϕ 0 ,i ) → 0 and therefore L i → (1 -π i ) + π i ε ⊕ . This last result shows how, even if the luminosity predicted by the model is exceedingly large, a non-detection can occur if the GW source is not real (1 -π i ), or if it is real but occulted by the Earth ( π i ε ⊕ ). Since the analysis is focused only on BBHevents, we consider only those candidates that have p BBH > p NSBH , p BNS . By definition, p BBH + p NSBH + p BNS = p astro and typically for the candidates classified as BBH we have that p BBH ≫ p NSBH , p BNS . The last condition allows us to consider Eq. (16) still valid if we replace π i with p BBH ,i , since the contribution of p NSBH ,i and p BNS ,i to the non-detection probability is negligible.  \nGiven the definition of Eq. (16), L i indicates the probability, given a set of ( λ 1 , ..., λ k ) EM parameters, that the BAT upper limit is not violated, taking into account the possible non-astrophysical origin of the candidate and also the probability that, even if astrophysical, the source is occulted by the Earth and therefore not detectable by Swift . Having a collection of E 1 , ..., E N GW candidates, the posterior distribution of the model parameters can be obtained following the Bayes theorem  \nP ( L 0 , f | E 1 , ..., E N ) = N ∏ i =1 L i π ( L 0 ) π ( f ) / ∫ N ∏ i =1 L i π ( L 0 ) π ( f )d L 0 d f,  \n(17)  \nwhere π ( L 0 ) and π ( f ) are the prior distributions of L 0 and f . We assume a log-uniform prior for both L 0 and f in the respective intervals 46 < log 10 [ L 0 (erg s -1 )] < 53 and -3 < log 10 ( f ) < 0. The choice of the prior boundaries are poorly informed by theoretical expectations, which are still affected by large uncertainties. Instead, the priors are chosen on the basis of the typical range of upper limit luminosity derived in this work for BBH events and the total number of candidates considered in this analysis. The constraints reported in the following may strongly depend on the choice of the prior boundaries. Therefore, the final goal of this simulation, more than deriving strong limits on the putative EM model, is to show the predictive power of the present analysis in the context of model inference and how this analysis can improve with the addition of more GW events in the future.  \nIn the specific case of our simulation, we consider all the GW candidates released in GWTC-3 (Abbott et al. 2023), including both the above threshold ( p astro > 0 . 5)  \nL  \nL  \nFigure 7. Constraints on the two parameters L 0 and f of the model for the putative EM counterpart of BBH mergers. The color map reports the likelihood L , for the full analysis including all the O3 catalog events with p astro > 0 . 5. L 0 is in units of erg s -1 . The thick blue solid and dashed contours indicate the exclusion regions in the [ L 0 , f ] plane at 90% and 50% credibility levels, respectively. The magenta solid and dashed lines report the same contours, but for an analysis that includes all O3 catalog events, with no cut in p astro .  \n<!-- image -->  \nand the subthreshold ( p astro < 0 . 5) candidates. For the latter, we emphasize that the classification of the CBC candidate as BBH merger is valid under the condition that the subthreshold GW event is of astrophysical origin. The simulation described in this section is set up in such a way that this assumption is taken into account for the final constraints of the physical parameters. All the low-latency candidates not confirmed by the offline analysis are not included in the simulation. The considered BBH sample with full NITRATES results and available flux upper limits consists of 32 events, 12 of which with p astro > 0 . 5.  \nIn order to compute numerically the functional behavior of the likelihood, we set up a simulation to evaluate P ( L 0 , f | E 1 , ..., E N ) in the full [ L 0 , f ] plane defined by the prior boundaries. The details of the simulation setup are reported in Appendix B. The results of the simulation for the sample of above threshold BBH candidates are reported in Fig. 7, where the color map indicates the value of L = ∏ L i , normalized by the maximum max( L ) over the full domain. The contour levels defining the 50% and 90% exclusion regions are reported as well. For comparison, in Fig. 7 we include also the same contour levels obtained with an analysis that considers all the BBH candidates without imposing any cut on the p astro . The level of constraining power of the analysis can be quantified by defining the fraction of the  \nFigure 8. Posterior distribution of log 10 ( f ), including the 50% and 90% upper limits with dot-dashed and dashed lines, respectively. The function is derived from Fig. 7, marginalizing over L 0 .  \n<!-- image -->  \nFigure 9. Posterior distribution of log 10 ( L 0 ), including the 50% and 90% upper limits with the dot-dashed and dashed lines, respectively. The function is derived from Fig. 7, marginalizing over f .  \n<!-- image -->  \nfull parameter space excluded with a credibility level η , corresponding to:  \nR η = I η /I tot , (18)  \nwhere I tot = ∫ d f d L 0 , being the integral extended to the full parameters domain, and with  \nI η = ∫ S d f d L 0 , (19)  \ncorresponding to the dimension of the region S of the parameter space excluded with a credibility level η . The  \nanalysis performed using only BBH with p astro > 0 . 5 gives a R 90% = 22%, while the analysis performed with the inclusion of subthreshold BBH candidates gives R 90% = 10%. This result indicates that with the inclusion of GW subthreshold events the analysis allows us to exclude a smaller portion of the parameter space, with respect to an analysis carried out using only events with high p astro . Therefore, even adding further information with the inclusion of more GW events, if these last are likely originated by noise, the full Bayesian analysis is affected by more uncertainty, resulting in an overall less constraining power of the analysis.  \nFigs. 8 and 9 report the posterior distribution of P ( f ) and P ( L 0 ), respectively, obtained as:  \nP ( f ) = ∫ P ( L 0 , f | E 1 , ..., E N )d L 0 (20)  \nand  \nP ( L 0 ) = ∫ P ( L 0 , f | E 1 , ..., E N )d f. (21)  \nBoth P ( L 0 ) and P ( f ) are normalized such that max[ P ( L 0 )] = max[ P ( f )] = 1. The posterior is reported in magenta and blue for both samples, with and without cut in p astro , respectively. The 50% and 90% upper limits are reported as well. From the shape of the posteriors and the values of the 50% and 90% upper limits, it is evident that both the L 0 and f posteriors only slightly differ from the flat priors, if no cut in p astro is applied. On the other hand, for the above threshold sample, the analysis is more informative and more stringent constraints on the parameters can be obtained, especially for L 0 . For the sample with p astro > 0 . 5, the 50% and 90% upper limits for f are log 10 ( f 50% ) = -1 . 67 and log 10 ( f 90% ) = -0 . 42, while for L 0 are log 10 [ L 0 , 50% (erg s -1 )] = 47 . 8 and log 10 [ L 0 , 90% (erg s -1 )] = 49 . 2, respectively.  \nIn the limit of a collection of triggers which correspond only to non-astrophysical events, i.e., all with π i = 0, the likelihood is constant in the full parameter space, not allowing to infer any constraints on the EM model parameters. On the other hand, if we increase the fraction of confident GW events and we keep fixed the total number N , their distance distribution P ( D L ) and the derived upper limits, then we obtain that L decreases accordingly. This implies that increasing the number of events with π i close to 1, the overall exclusion region in the ( λ 1 , ..., λ k ) parameter space increases as well. This demonstrates that with the collection of more data, in the limit of a GW detector horizon constant in time, this method allows us to improve incrementally our constraints on the EM models of CBC events. Although, realistically the GW detection horizon will increase with time (Abbott et al. 2020), implying an overall increase of  \nthe median values of D L of the candidate events. Such an effect increases in turn the values of the luminosity upper limits, increasing as well the values of P i ( ϕ < ϕ 0 ) and hence of L . This effect tends to decrease the dimension of the exclusion region of the ( λ 1 , ..., λ k ) parameter space. Overall, the final outcome of the inclusion of additional GW data, in terms of the constraining power of this analysis, will depend on the simultaneous combined effect of increasing the number of confident events and of increasing the detection horizon.  \nIn order to show how the inclusion of more significant GW candidates can improve the constraining power of the present analysis, we carried out the following simulation. We repeated the same procedure adopted to produce the exclusion regions of Fig. 7, but replacing the real π i with π i = 1 for all the confirmed BBH candidates, hence imposing that they are all significant events. All the values of ϕ UL , ε ⊕ and P ( D L ) of each candidate are left unchanged. The resulting 50% and 90% exclusion regions are reported in Fig. 10, with black dashed and solid lines, respectively. The fraction of the 90% excluded region increases to a value of R 90% = 17%, clearly demonstrating that, even if the BAT flux upper limit are the same, the increase of confidence about the astrophysical nature of the GW improves our final constraints on the model parameter space. Furthermore, Fig. 10 reports also the 50% and 90% exclusion regions (with red dashed and solid lines, respectively), obtained as before, but imposing both π i = 1 and ε ⊕ = 0 for each candidate. This combination corresponds to simulate all real BBH candidates, whose sky localization does not overlap with the sky region covered by the Earth. In this case R 90% = 35%, showing that also the fraction of GW sky posterior occulted by Earth has a significant impact on our final results.', '7. CONCLUSIONS': 'In this work we report the systematic search of signals jointly detected by the LIGO-Virgo interferometers and the Swift -BAT telescope, during the third LVK observing run. Thanks to the prompt availability of BAT data using GUANO and the sensitive targeted search capabilities of the NITRATES pipeline, we conducted deep follow-up searches for EM signals on a sample of 636 GW triggers. The search results did not yield any confident joint detection, allowing us to derive upper limits in the 15-350 keV band. We provide comprehensive details on all analyzed GW triggers along with their NITRATES search statistics. This information can be valuable for calibrating and comparing with other offline targeted search pipelines that are currently operational or may be developed in the future.  \nL  \nL  \nFigure 10. Same as Fig. 7, but simulating all the O3 catalog candidates with an associated p astro = π i = 1. The black dashed and solid lines identify the 50% and 90% exclusion regions, respectively. The red dashed and solid lines have the same meaning, but derived imposing both π i = 1 and ε ⊕ = 0.  \n<!-- image -->  \nIn the specific case of the BBH class, the BAT flux upper limits have been used to perform a stacking analysis and to derive constraints on the possible nature of an associated EM emission. As illustrated in Section 6, the presence of several BBH candidates with large values of p astro in our sample, enhances our ability to better constrain the parameter space for EM emission from BH mergers, with minimal assumptions on our prior knowledge of the nature of the emission. The prospect of detecting EM emission from BBH mergers has been debated and discussed in detail in recent times. Particularly, the GBM trigger that accompanied the first BBH merger event, GW150914, has served as a case study to test possible association and potential implications (Abbott et al. 2016b; Connaughton et al. 2016; Goldstein et al. 2016b). Though not likely, there are a number of physical models that have been proposed that could give rise to detectable emission in the gamma-ray band. A summary of the various different models has been discussed in Fletcher et al. (2023) and Veres et al. (2019). The models involve parameters pertaining to potential remnant accretion effects, magnetic field strength, black hole charge and spin, among others (e.g., Loeb 2016; Dai et al. 2017; Woosley 2016; Zhang 2016). The method described in Section 6 can be easily extended to any of these models, provided that the luminosity function Π( L ) of the putative BBH EM emission is known. Additionally, effects possibly related to the viewing angle dependency of the EM emission can be easily included in this approach. Regarding CBCs containing at least one  \nNS (BNS and NSBH), it was not possible to conduct a similar stacking analysis as the one described for BBH in Section 6, due to the paucity of such events with a large enough value of p astro . Further observations, including the fourth LVK observing run (O4), could lead to the collection of a larger number of BNS and NSBH candidates with moderate values of p astro , giving the possibility to repeat the analysis performed in this paper and to derive informative constraints on the EM emission of these classes and the properties of the associated GRB populations. Data products associated with the present analysis are reported in a separate data release. 7  \nO4 commenced on the 24th of May, 2023. The number of significant detections is expected to increase by several times during the entire duration of O4 (Abbott et al. 2018; Petrov et al. 2022). Targeted search results using the GUANO-NITRATES infrastructure are publicly available in real-time 8 . In the case of non-detection of an EM counterpart, the GUANO team reports the 15-350 keV flux upper limit for all the GW triggers classified as significant , via GCN Circulars. Additional enhancements to the likelihood search code have reduced the search latency by a factor of 2, with respect to O3.  \nThanks to its sensitivity in the hard X-ray band and the possibility to localize EM transients down to a precision of an arcminute, Swift represents one of the main discovery machines for the detection of EM counterparts of GW transients. This paper shows how the GUANO infrastructure has a deep impact on the multi-messenger science case, in particular for optimally exploiting the sensitivity of the Swift -BAT instrument for the detection of EM counterparts of CBCs detected by the LVK Collaboration. The deep subthreshold search enabled by the NITRATES pipeline sensibly increases the detection horizon of Swift , giving the chance to detect transients also outside the BAT FOV and allowing us to possibly detect faint X-ray/gamma-ray transients associated to relativistic jets observed off-axis, as in the case of GW170817. In the case of a confident joint Swift -GW detection, the GUANO team will promptly disseminate all the information about the EM candidate via GCN Circulars, providing an estimate of the sky localization when available. Moreover, also in the case of non-detection, this paper shows how the upper limits derived from the NITRATES analysis can be combined to have the most sensitive constraints on the EM emission from all the CBC classes. The cumulative collection of  \nnon-detection will gradually improve our knowledge of the EM nature of CBCs.', 'ACKNOWLEDGMENTS': "Gayathri Raman, Samuele Ronchini, and Jamie Kennea acknowledge the support of NASA grants 80NSSC19K0408 and 80NSSC22K1498 awarded as part of the NASA Neil Gehrels Swift Observatory Guest Investigator program. Jamie Kennea and James Delaunay acknowledge the support of NASA contract NAS5-0136.  \nThis material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors also gratefully acknowledge the support of the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO 600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. The authors gratefully acknowledge the Italian Istituto Nazionale di Fisica Nucleare (INFN), the French Centre National de la Recherche Scientifique (CNRS) and the Netherlands Organization for Scientific Research (NWO), for the construction and operation of the Virgo detector and the creation and support of the EGO consortium. The authors also gratefully acknowledge research support from these agencies as well as by the Council of Scientific and Industrial Research of India, the Department of Science and Technology, India, the Science & Engineering Research Board (SERB), India, the Ministry of Human Resource Development, India, the Spanish Agencia Estatal de Investigaci'on (AEI), the Spanish Ministerio de Ciencia, Innovaci'on y Universidades, the European Union NextGenerationEU/PRTR (PRTR-C17.I1), the ICSC - CentroNazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by the European Union NextGenerationEU, the Comunitat Auton'oma de les Illes Balears through the Direcci'o General de Recerca, Innovaci'o i Transformaci'o Digital with funds from the Tourist Stay Tax Law ITS 2017-006, the Conselleria d'Economia, Hisenda i Innovaci'o, the FEDER Operational Program 2021-2027 of the Balearic Islands, the Conselleria d'Innovaci'o, Universitats, Ci'encia i Societat Digital de la Generalitat Valenciana and the CERCA Programme Generalitat de Catalunya, Spain, the National Science Centre of Poland and the European Union - European Regional Development Fund; Foundation for Polish Science (FNP), the Polish Ministry of Science and Higher Education, the Swiss National Science Foundation (SNSF), the Russian Science Foun-  \nthe European Commission, the European Social Funds (ESF), the European Regional Development Funds (ERDF), the Royal Society, the Scottish Funding Council, the Scottish Universities Physics Alliance, the Hungarian Scientific Research Fund (OTKA), the French Lyon Institute of Origins (LIO), the Belgian Fonds de la Recherche Scientifique (FRS-FNRS), Actions de Recherche Concert'ees (ARC) and Fonds Wetenschappelijk Onderzoek - Vlaanderen (FWO), Belgium, the Paris ˆ Ile-de-France Region, the National Research, Development and Innovation Office Hungary (NKFIH), the National Research Foundation of Korea, the Natural Science and Engineering Research Council Canada, Canadian Foundation for Innovation (CFI), the Brazilian Ministry of Science, Technology, and Innovations, the International Center for Theoretical Physics South American Institute for Fundamental Research (ICTPSAIFR), the Research Grants Council of Hong Kong, the National Natural Science Foundation of China (NSFC), the Leverhulme Trust, the Research Corporation, the National Science and Technology Council (NSTC), Taiwan, the United States Department of Energy, and the Kavli Foundation. The authors gratefully acknowledge the support of the NSF, STFC, INFN and CNRS for provision of computational resources.  \nThis work was supported by MEXT, JSPS Leadingedge Research Infrastructure Program, JSPS Grant-inAid for Specially Promoted Research 26000005, JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2905: JP17H06358, JP17H06361 and JP17H06364, JSPS Core-to-Core Program A. Advanced Research Networks, JSPS Grant-in-Aid for Scientific Research (S) 17H06133 and 20H05639 , JSPS Grant-in-Aid for Transformative Research Areas (A) 20A203: JP20H05854, the joint research program of the Institute for Cosmic Ray Research, University of Tokyo, National Research Foundation (NRF), Computing Infrastructure Project of Global Science experimental Data hub Center (GSDC) at KISTI, Korea Astronomy and Space Science Institute (KASI), and Ministry of Science and ICT (MSIT) in Korea, Academia Sinica (AS), AS Grid Center (ASGC) and the National Science and Technology Council (NSTC) in Taiwan under grants including the Rising Star Program and Science Vanguard Research Program, Advanced Technology Center (ATC) of NAOJ, and Mechanical Engineering Center of KEK.  \nAdditional acknowledgements for support of individual authors may be found in the following document: https://dcc.ligo.org/LIGO-M2300033/public . For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising. We  \nrequest that citations to this article use 'A. G. Abac et al. (LIGO-Virgo-KAGRA Collaboration), ...' or similar phrasing, depending on journal convention.  \nMatplotlib (Hunter 2007), SEABORN (Waskom 2021), NumPy (Harris et al. 2020) and SciPy (Virtanen et al. 2020) were used in the preparation of the manuscript.", 'REFERENCES': 'where λ is the credibility level of the contour.  \n- 4. The contour is drawn imposing L = L ( ˆ L 0 , ˆ f ).', 'A. LUMINOSITY UPPER LIMIT': 'A more accurate method to derive the luminosity upper limit should be based on the knowledge of P ( D L ) and P ( ϕ ), where ϕ is the flux measured in the BAT energy band. Having only an upper limit, P ( ϕ ) can be approximated as  \nP ( ϕ ) ∝    Π( ϕ ) , ϕ < ϕ UL 0 , ϕ > ϕ UL , (A1)  \nwhere Π( ϕ ) is our prior distribution for the flux. Using the conversion from flux to luminosity L = 4 πD 2 L ϕ , the probability distribution of the luminosity can be computed as  \nP ( L ) = P (4 πD 2 L ϕ ) ∝ ∫ 1 ϕ P ϕ ( ϕ ) P D 2 L ( L 4 πϕ ) d ϕ, (A2)  \nwhere P ϕ is the flux probability distribution and P D 2 L is the probability distribution of D 2 L . In the conversion from flux to luminosity, the k-correction has been omitted, since it introduces a mild dependence on the redshift, which is not relevant for the purposes of this section. The 5 σ luminosity upper limit L UL can be found imposing that  \n∫ L UL 0 P ( L )d L = 1 -ε 5 σ , (A3)  \nwith ε 5 σ = 3 × 10 -7 . The value of L UL has been computed adopting two different assumptions for the flux prior, corresponding to Π( ϕ ) ∝ const. and Π( ϕ ) ∝ ϕ -3 / 2 , with the latter being inspired by the usual trend followed by GRBs (e.g., Salafia et al. 2023). In both cases, we find that L UL ∼ 3 × 4 π ⟨ D 2 L ⟩ ϕ UL .', 'B. SIMULATION SETUP': 'In this section we specify the details of the simulation used to compute numerically the L ( L 0 , f | E 1 , ..., E N ) function. For each simulated GW candidate, the single L i is computed for each pairs of values ( L 0 ,n , f m ). The flux predicted by the EM model is predicted injecting 1000 sources whose luminosity distance is distributed according to P ( D L ), derived from the GW localization. The probability P i ( ϕ < ϕ 0 ,i ) is derived computing the fraction of cases that have a flux below the sky-averaged BAT upper limit, defined by Eq. (3). The computation of L is performed on a 100 × 100 grid of ( L 0 ,n , f m ). Once the previous steps are performed for all the GW candidates, the final combined likelihood is computed as  \nL ( L 0 , f | E 1 , ..., E N ) = ∏ i L i ( L 0 , f ) . (B4)  \nIn order to produce the credibility contours in the [ L 0 ,n , f m ] plane, we adopt the following steps:  \n- 1. L is normalized such that  \n∑ n,m L ( L 0 ,n , f m ) = 1 . (B5)  \n- 2. A one-dimensional array L [ x n ] is created flattening the two-dimensional grid L ( L 0 ,n , f m ), then L [ x n ] is sorted in ascending order.\n- 3. We find the element [ ˆ L 0 , ˆ f ] = [ x n ∗ ] such that  \nn ∗ ∑ n =0 L [ x n ] = λ, (B6)', 'C. FLUX UPPER LIMIT DERIVATION': 'In this appendix we show an alternative method to compute the non-detection likelihood presented in Section 6. The NITRATES analysis allows us to derive a flux upper limit at a given confidence level for each pixel of the GW sky localization, corresponding to the function ϕ UL (RA , Dec) defined in Eq. (3). Then combined probability of being located in the pixel x i and to have a non-detectable EM emission is  \nP (non-det , x i ) ∝ P GW ( x i ) P [ ϕ < ϕ UL ( x i )]∆Ω i , (C7)  \nwhere  \nP [ ϕ < ϕ UL ( x i )] = (1 -f ) + f ∫ ϕ UL ( x i ) 0 P ( ϕ | x i )d ϕ, (C8)  \nand ∆Ω i is the area of the pixel. Here we express P ( ϕ | x i ) as the conditional flux probability distribution, namely the flux probability distribution assuming that the GW source is contained in the pixel x i . To compute the P ( ϕ | x i ), for a fixed luminosity L , the luminosity distance is extracted from the the conditional probability distribution P ( D L | x i ), which is derived from the GW sky localization. Finally the overall non-detection probability is obtained integrating Eq. (C7) over the full sky:  \nP (non-det | f, L 0 ) = ∑ x i P GW ( x i ) P [ ϕ < ϕ UL ( x i )]∆Ω i = (1 -f )+ f [ ε ⊕ + ∑ x i / ∈ Ω ⊕ P GW ( x i )∆Ω i ∫ ϕ UL ( x i ) 0 P ( ϕ | x i )d ϕ ] , (C9)  \nwhere we have used that  \n∫ ϕ UL ( x i ) 0 P ( ϕ | x i )d ϕ = 1 if x i ∈ ⊕ , and ∑ x i ∈ Ω ⊕ P GW ( x i )∆Ω i = ε ⊕ . (C10)  \nThe resulting probability of non-detecting any EM emission in correspondence to a GW trigger with a given p astro = π i is  \nP (non-det | f, L 0 , π i ) = (1 -π i ) + π i P (non-det | f, L 0 ) . (C11)  \nEq. (C9) has to be compared with the method used in Section 6, where instead we used the approximation:  \nP (non-det | f, L 0 ) = (1 -f ) + f ∫ ϕ UL 0 P ( ϕ )d ϕ, (C12)  \nϕ UL = ∫ Ω / ∈ Ω ⊕ ϕ UL (Ω) P GW (Ω)dΩ , (C13)  \nwith  \nand P ( ϕ ) is obtained extracting D L from the full sky marginalized distribution P ( D L ), corresponding to  \nP ( D L ) = ∑ x i P GW ( x i ) P ( D L | x i )∆Ω i . (C14)  \nThe two methods give comparable results in the assumption that the following approximation is valid:  \n∫ ϕ UL 0 P ( ϕ )d ϕ ≈ ε ⊕ + ∑ x i / ∈ Ω ⊕ P GW ( x i )∆Ω i ∫ ϕ UL ( x i ) 0 P ( ϕ | x i )d ϕ. (C15)  \nFor completeness, we clarify here the main differences in the two methods.', 'METHOD 1:': 'This is the method used in Section 6 and is based on the following steps:  \n- 1. The marginalized upper limit ϕ UL is computed over the full sky, weighting by the GW sky localization.\n- 2. Once L 0 is fixed, the flux probability distribution P ( ϕ ) is computed extracting randomly D L from the P ( D L ), the latter corresponding to the posterior distribution of the GW luminosity distance, marginalized over the full sky (excluding the part occulted by the Earth).\n- 3. The integral ∫ ϕ UL 0 P ( ϕ )d ϕ which appears in Eq. (C12) is evaluated counting the fraction of simulated events that have a predicted flux below the sky-averaged upper limit ϕ UL .', 'METHOD 2:': 'This is the method presented in this appendix and summarized by Eqs. (C9) and (C11), consisting in the following procedure:  \n- 1. A set of sources is injected in space and the distribution follows the volumetric probability distribution of the GW candidate. First the coordinates of the injected source are extracted from the sky localization P GW (RA , Dec), then for each position the distance is extracted according to the conditional probability P ( D L | RA , Dec).\n- 2. For each injected source, once the luminosity L 0 is fixed, the predicted flux is compared with the coordinatesdependent BAT upper limit map ϕ UL (RA , Dec).\n- 3. We define ρ / ∈⊕ the fraction of all the sources injected which are not occulted by the Earth and also have a predicted flux below ϕ UL (RA , Dec). Given this definition, we have:  \n∑ x i / ∈ Ω ⊕ P GW ( x i )∆Ω i ∫ ϕ UL ( x i ) 0 P ( ϕ | x i )d ϕ = (1 -ε ⊕ ) ρ / ∈⊕ . (C16)  \nThe last equality can be justified considering that, if for each pixel i we inject N tot ,i sources, we can define ρ i = N ND ,i /N tot , i , where N ND ,i is the fraction of injected sources that are not detected, i.e., with a predicted flux below ϕ UL ( x i ). Therefore:  \n∫ ϕ UL ( x i ) 0 P ( ϕ | x i )d ϕ = ρ i . (C17)  \nLet us call N tot the total number of sources injected on the full sky. Then we have  \nN tot , i = N tot P GW ( x i )∆Ω i , (C18)  \nand therefore  \n∑ x i / ∈ Ω ⊕ P GW ( x i )∆Ω i ∫ ϕ UL ( x i ) 0 P ( ϕ | x i )d ϕ = ∑ x i / ∈ Ω ⊕ N tot ,i N tot ρ i = 1 N tot ∑ x i / ∈ Ω ⊕ N ND ,i . (C19)  \nSince the total number of injected sources not occulted by Earth are N tot , / ∈⊕ = (1 -ε ⊕ ) N tot , and using that  \nρ / ∈⊕ = 1 N tot , / ∈⊕ ∑ x i / ∈ Ω ⊕ N ND ,i , (C20)  \nwe finally recover Eq. (C16).  \nIn order to quantify the difference between the two methods, the following test is performed. Having fixed the two parameters f and L 0 , we compute the likelihood L for the two methods and we derive the quantity  \nε L = 2 abs( L 1 -L 2 ) L 1 + L 2 . (C21)  \nHere we use the subscripts 1 and 2 for the respective methods. Both likelihoods are computed considering only BBH candidates with p astro > 0 . 5. In both cases, the total number of injected sources for each BBH candidate is N tot = 1000. The distribution of ε L is evaluated sampling randomly f and L 0 , for a total of 100 sampled pairs ( f, L 0 ). We obtain that the median value of ε L is 0.04 and that in ∼ 80% of the sampled cases ε L < 0 . 2. Since the difference between the two methods is limited and since the Method 2 is more computationally expensive, all the results are used adopting Method 1.  \n√  \nTable 1 . List of 636 low latency GW triggers analyzed using NITRATES is shown along with their respective p astro values and 15-350 keV band flux upper limits. The maximum √ TS is indicated for all the triggers with successful NITRATES results. Observations corresponding to triggers with insufficient exposure time during the BAT pointing mode do not have valid NITRATES results or flux upper limits. For those triggers that do have NITRATES results but fail to meet the criterion for a full likelihood analysis, the max √ TS is indicated as NFL (No Final Likelihood). The GW triggers from the Burst pipeline do not have associated p astro values and are therefore left blank. The fraction of the GW sky posterior distribution inside the BAT coded FOV and the fraction of the GW posterior occulted by the Earth are denoted by ε in BAT and ε ⊕ , respectively.  \nTable 2. Details of the O3 candidates confirmed by the offline analysis and with a p astro > 0 . 5, for which GUANO data dumps are available. The reported p astro and FAR are relative to the pipeline with the highest p astro . If two pipelines have equal p astro , we select the one with the highest SNR. The GW FAR, p astro and Class details are quoted from Abbott et al. (2024) and Abbott et al. (2023).  \nTable 3 . List of the O3 candidates confirmed by the offline analysis with p astro < 0 . 5, for which GUANO data dumps were available. GW FAR, p astro , Class and p Class are reported from Abbott et al. (2023). CBC or Burst group categories are quoted as per the offline analysis and not from the low-latency information.  \n√  \nTable 4. Details of the joint FAR computed according to the procedure detailed in Section 5.4 for all the triggers with FAR GRB , max < 10 -3 Hz. The RAVEN alert is, by definition, evaluated considering only information received in low latency. The events marked with a (*) are GW candidates with p astro > 0 . 5.'}