11institutetext: Yerevan Physics Institute, Yerevan, Armenia22institutetext:
Université Libre de Bruxelles, Brussels, Belgium33institutetext: Universidade
Estadual de Londrina, Londrina, Brazil44institutetext: Pontificia Universidade
Catolica do Rio de Janeiro, Rio, Brazil55institutetext: Pontificia Universidad
Católica de Chile, Santiago, Chile66institutetext: Universidad Tecnica
Federico Santa Maria, Valparaiso, Chile77institutetext: Beijing Institute of
Spacecraft Environment Engineering, Beijing, China88institutetext: Beijing
Normal University, Beijing, China99institutetext: China Institute of Atomic
Energy, Beijing, China1010institutetext: Institute of High Energy Physics,
Beijing, China1111institutetext: North China Electric Power University,
Beijing, China1212institutetext: School of Physics, Peking University,
Beijing, China1313institutetext: Tsinghua University, Beijing,
China1414institutetext: University of Chinese Academy of Sciences, Beijing,
China1515institutetext: Jilin University, Changchun, China1616institutetext:
College of Electronic Science and Engineering, National University of Defense
Technology, Changsha, China1717institutetext: Chongqing University, Chongqing,
China1818institutetext: Dongguan University of Technology, Dongguan,
China1919institutetext: Jinan University, Guangzhou, China2020institutetext:
Sun Yat-Sen University, Guangzhou, China2121institutetext: Harbin Institute of
Technology, Harbin, China2222institutetext: University of Science and
Technology of China, Hefei, China2323institutetext: The Radiochemistry and
Nuclear Chemistry Group in University of South China, Hengyang,
China2424institutetext: Wuyi University, Jiangmen, China2525institutetext:
Shandong University, Jinan, China, and Key Laboratory of Particle Physics and
Particle Irradiation of Ministry of Education, Shandong University, Qingdao,
China2626institutetext: Institute of Modern Physics, Chinese Academy of
Sciences, Lanzhou, China2727institutetext: Nanjing University, Nanjing,
China2828institutetext: Guangxi University, Nanning, China2929institutetext:
East China University of Science and Technology, Shanghai,
China3030institutetext: School of Physics and Astronomy, Shanghai Jiao Tong
University, Shanghai, China3131institutetext: Tsung-Dao Lee Institute,
Shanghai Jiao Tong University, Shanghai, China3232institutetext: Institute of
Hydrogeology and Environmental Geology, Chinese Academy of Geological
Sciences, Shijiazhuang, China3333institutetext: Nankai University, Tianjin,
China3434institutetext: Wuhan University, Wuhan, China3535institutetext: Xi’an
Jiaotong University, Xi’an, China3636institutetext: Xiamen University, Xiamen,
China3737institutetext: School of Physics and Microelectronics, Zhengzhou
University, Zhengzhou, China3838institutetext: Institute of Physics, National
Yang Ming Chiao Tung University, Hsinchu3939institutetext: National United
University, Miao-Li4040institutetext: Department of Physics, National Taiwan
University, Taipei4141institutetext: Charles University, Faculty of
Mathematics and Physics, Prague, Czech Republic4242institutetext: University
of Jyvaskyla, Department of Physics, Jyvaskyla, Finland4343institutetext:
IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405 Orsay,
France4444institutetext: Univ. Bordeaux, CNRS, CENBG, UMR 5797, F-33170
Gradignan, France4545institutetext: IPHC, Université de Strasbourg,
CNRS/IN2P3, F-67037 Strasbourg, France4646institutetext: Centre de Physique
des Particules de Marseille, Marseille, France4747institutetext: SUBATECH,
Université de Nantes, IMT Atlantique, CNRS-IN2P3, Nantes,
France4848institutetext: III. Physikalisches Institut B, RWTH Aachen
University, Aachen, Germany4949institutetext: Institute of Experimental
Physics, University of Hamburg, Hamburg, Germany5050institutetext:
Forschungszentrum Jülich GmbH, Nuclear Physics Institute IKP-2, Jülich,
Germany5151institutetext: Forschungszentrum Jülich GmbH, Central Institute of
Engineering, Electronics and Analytics - Electronic Systems (ZEA-2), Jülich,
Germany5252institutetext: Institute of Physics, Johannes-Gutenberg Universität
Mainz, Mainz, Germany5353institutetext: Technische Universität München,
München, Germany5454institutetext: Eberhard Karls Universität Tübingen,
Physikalisches Institut, Tübingen, Germany5555institutetext: INFN Catania and
Dipartimento di Fisica e Astronomia dell Università di Catania, Catania,
Italy5656institutetext: Department of Physics and Earth Science, University of
Ferrara and INFN Sezione di Ferrara, Ferrara, Italy5757institutetext: INFN
Sezione di Milano and Dipartimento di Fisica dell Università di Milano,
Milano, Italy5858institutetext: INFN Milano Bicocca and Università di Milano-
Bicocca, Milano, Italy5959institutetext: INFN Milano Bicocca and Politecnico
of Milano, Milano, Italy6060institutetext: INFN Sezione di Padova, Padova,
Italy6161institutetext: Dipartimento di Fisica e Astronomia dell’Università di
Padova and INFN Sezione di Padova, Padova, Italy6262institutetext: INFN
Sezione di Perugia and Dipartimento di Chimica, Biologia e Biotecnologie
dell’Università di Perugia, Perugia, Italy6363institutetext: Laboratori
Nazionali di Frascati dell’INFN, Roma, Italy6464institutetext: University of
Roma Tre and INFN Sezione Roma Tre, Roma, Italy6565institutetext: Institute of
Electronics and Computer Science, Riga, Latvia6666institutetext: Pakistan
Institute of Nuclear Science and Technology, Islamabad,
Pakistan6767institutetext: Joint Institute for Nuclear Research, Dubna,
Russia6868institutetext: Institute for Nuclear Research of the Russian Academy
of Sciences, Moscow, Russia6969institutetext: Lomonosov Moscow State
University, Moscow, Russia7070institutetext: Comenius University Bratislava,
Faculty of Mathematics, Physics and Informatics, Bratislava,
Slovakia7171institutetext: Department of Physics, Faculty of Science,
Chulalongkorn University, Bangkok, Thailand7272institutetext: National
Astronomical Research Institute of Thailand, Chiang Mai,
Thailand7373institutetext: Suranaree University of Technology, Nakhon
Ratchasima, Thailand7474institutetext: Department of Physics and Astronomy,
University of California, Irvine, California, USA

# Radioactivity control strategy for the JUNO detector

Angel Abusleme 45 Thomas Adam 66 Shakeel Ahmad 66 Rizwan Ahmed 55 Sebastiano
Aiello 66 Muhammad Akram 29 Fengpeng An 22 Qi An 55 Giuseppe Andronico 67
Nikolay Anfimov 57 Vito Antonelli 67 Tatiana Antoshkina 71 Burin Asavapibhop
45 João Pedro Athayde Marcondes de André 43 Didier Auguste 70 Andrej Babic 56
Wander Baldini 58 Andrea Barresi 57 Davide Basilico 45 Eric Baussan 60 Marco
Bellato 60 Antonio Bergnoli 48 Thilo Birkenfeld 43 Sylvie Blin 54 David Blum
40 Simon Blyth 67 Anastasia Bolshakova 47 Mathieu Bongrand 44,40 Clément
Bordereau 43 Dominique Breton 57 Augusto Brigatti 61 Riccardo Brugnera 55
Riccardo Bruno 64 Antonio Budano 55 Mario Buscemi 46 Jose Busto 67 Ilya
Butorov 43 Anatael Cabrera 34 Hao Cai 10 Xiao Cai 10 Yanke Cai 10 Zhiyan Cai
59 Antonio Cammi 5 Agustin Campeny 10 Chuanya Cao 10 Guofu Cao 10 Jun Cao 55
Rossella Caruso 44 Cédric Cerna 10 Jinfan Chang 39 Yun Chang 18 Pingping Chen
40 Po-An Chen 13 Shaomin Chen 26 Xurong Chen 38 Yi-Wen Chen 11 Yixue Chen 20
Yu Chen 10 Zhang Chen 10 Jie Cheng 7 Yaping Cheng 67 Alexey Chetverikov 58
Davide Chiesa 3 Pietro Chimenti 67 Artem Chukanov 44 Gérard Claverie 62 Catia
Clementi 2 Barbara Clerbaux 44 Selma Conforti Di Lorenzo 60 Daniele Corti 58
Oliviero Cremonesi 60 Flavio Dal Corso 74 Olivia Dalager 44 Christophe De La
Taille 34 Jiawei Deng 13 Zhi Deng 10 Ziyan Deng 52 Wilfried Depnering 5 Marco
Diaz 57 Xuefeng Ding 10 Yayun Ding 73 Bayu Dirgantara 67 Sergey Dmitrievsky 41
Tadeas Dohnal 67 Dmitry Dolzhikov 69 Georgy Donchenko 13 Jianmeng Dong 68
Evgeny Doroshkevich 45 Marcos Dracos 44 Frédéric Druillole 37 Shuxian Du 60
Stefano Dusini 41 Martin Dvorak 42 Timo Enqvist 52 Heike Enzmann 64 Andrea
Fabbri 70 Lukas Fajt 24 Donghua Fan 10 Lei Fan 10 Jian Fang 10 Wenxing Fang 55
Marco Fargetta 67 Dmitry Fedoseev 70 Vladko Fekete 38 Li-Cheng Feng 21 Qichun
Feng 57 Richard Ford 57 Andrey Formozov 44 Amélie Fournier 32 Haonan Gan 48
Feng Gao 61 Alberto Garfagnini 57 Marco Giammarchi 61,a Agnese Giaz111Present
address: Università dell’Insubria, Como, Italy. 55 Nunzio Giudice 67 Maxim
Gonchar 13 Guanghua Gong 13 Hui Gong 67 Yuri Gornushkin 50,48 Alexandre Göttel
61 Marco Grassi 51 Christian Grewing 67 Vasily Gromov 10 Minghao Gu 37 Xiaofei
Gu 19 Yu Gu 10 Mengyun Guan 55 Nunzio Guardone 66 Maria Gul 10 Cong Guo 20
Jingyuan Guo 10 Wanlei Guo 8 Xinheng Guo 35,50 Yuhang Guo 52 Paul Hackspacher
49 Caren Hagner 7 Ran Han 20 Yang Han 66 Muhammad Sohaib Hassan 10 Miao He 10
Wei He 54 Tobias Heinz 44 Patrick Hellmuth 10 Yuekun Heng 5 Rafael Herrera 20
YuenKeung Hor 10 Shaojing Hou 40 Yee Hsiung 40 Bei-Zhen Hu 20 Hang Hu 10
Jianrun Hu 10 Jun Hu 9 Shouyang Hu 10 Tao Hu 20 Zhuojun Hu 20 Chunhao Huang 10
Guihong Huang 9 Hanxiong Huang 25 Wenhao Huang 10 Xin Huang 25 Xingtao Huang
28 Yongbo Huang 30 Jiaqi Hui 21 Lei Huo 22 Wenju Huo 44 Cédric Huss 66 Safeer
Hussain 1 Ara Ioannisian 60 Roberto Isocrate 61 Beatrice Jelmini 38 Kuo-Lun
Jen 5 Ignacio Jeria 10 Xiaolu Ji 20 Xingzhao Ji 33 Huihui Jia 34 Junji Jia 9
Siyu Jian 22 Di Jiang 10 Xiaoshan Jiang 10 Ruyi Jin 10 Xiaoping Jing 44 Cécile
Jollet 42 Jari Joutsenvaara 73 Sirichok Jungthawan 45 Leonidas Kalousis 50
Philipp Kampmann 18 Li Kang 47 Rebin Karaparambil 1 Narine Kazarian 35 Waseem
Khan 73 Khanchai Khosonthongkee 67 Denis Korablev 69 Konstantin Kouzakov 67
Alexey Krasnoperov 51 Andre Kruth 67 Nikolay Kutovskiy 42 Pasi Kuusiniemi 54
Tobias Lachenmaier 57 Cecilia Landini 44 Sébastien Leblanc 47 Victor Lebrin 47
Frederic Lefevre 18 Ruiting Lei 41 Rupert Leitner 38 Jason Leung 37 Demin Li
10 Fei Li 13 Fule Li 20 Haitao Li 10 Huiling Li 20 Jiaqi Li 10 Mengzhao Li 11
Min Li 10 Nan Li 16 Nan Li 16 Qingjiang Li 10 Ruhui Li 18 Shanfeng Li 20 Tao
Li 10,14 Weidong Li 10 Weiguo Li 9 Xiaomei Li 10 Xiaonan Li 9 Xinglong Li 18
Yi Li 10 Yufeng Li 10 Zhaohan Li 20 Zhibing Li 20 Ziyuan Li 9 Hao Liang 22 Hao
Liang 20 Jiajun Liao 51 Daniel Liebau 73 Ayut Limphirat 73 Sukit Limpijumnong
38 Guey-Lin Lin 18 Shengxin Lin 10 Tao Lin 20 Jiajie Ling 60 Ivano Lippi 11
Fang Liu 37 Haidong Liu 28 Hongbang Liu 23 Hongjuan Liu 20 Hongtao Liu 19 Hui
Liu 30,31 Jianglai Liu 10 Jinchang Liu 23 Min Liu 14 Qian Liu 22 Qin Liu 50,48
Runxuan Liu 10 Shuangyu Liu 22 Shubin Liu 10 Shulin Liu 20 Xiaowei Liu 28
Xiwen Liu 10 Yan Liu 10 Yunzhe Liu 69,68 Alexey Lokhov 57 Paolo Lombardi 55
Claudio Lombardo 52 Kai Loo 32 Chuan Lu 10 Haoqi Lu 15 Jingbin Lu 10 Junguang
Lu 37 Shuxiang Lu 10 Xiaoxu Lu 68 Bayarto Lubsandorzhiev 68 Sultim
Lubsandorzhiev 50,48 Livia Ludhova 10 Fengjiao Luo 20 Guang Luo 20 Pengwei Luo
36 Shu Luo 10 Wuming Luo 68 Vladimir Lyashuk 25 Bangzheng Ma 10 Qiumei Ma 10
Si Ma 10 Xiaoyan Ma 11 Xubo Ma 43 Jihane Maalmi 67 Yury Malyshkin 56 Fabio
Mantovani 61 Francesco Manzali 7 Xin Mao 12 Yajun Mao 64 Stefano M. Mari 61
Filippo Marini 66 Sadia Marium 64 Cristina Martellini 43 Gisele Martin-
Chassard 63 Agnese Martini 53 Matthias Mayer 1 Davit Mayilyan 65 Ints Mednieks
30 Yue Meng 44 Anselmo Meregaglia 57 Emanuela Meroni 49 David Meyhöfer 60
Mauro Mezzetto 6 Jonathan Miller 57 Lino Miramonti 64 Paolo Montini 56 Michele
Montuschi 54 Axel Müller 58 Massimiliano Nastasi 67 Dmitry V. Naumov 67 Elena
Naumova 43 Diana Navas-Nicolas 67 Igor Nemchenok 38 Minh Thuan Nguyen Thi 10
Feipeng Ning 10 Zhe Ning 4 Hiroshi Nunokawa 53 Lothar Oberauer 74,5 Juan Pedro
Ochoa-Ricoux 67 Alexander Olshevskiy 64 Domizia Orestano 62 Fausto Ortica 52
Rainer Othegraven 40 Hsiao-Ru Pan 63 Alessandro Paoloni 57 Sergio Parmeggiano
10 Yatian Pei 62 Nicomede Pelliccia 23 Anguo Peng 22 Haiping Peng 44 Frédéric
Perrot 2 Pierre-Alexandre Petitjean 64 Fabrizio Petrucci 52 Oliver Pilarczyk
45 Luis Felipe Piñeres Rico 69 Artyom Popov 45 Pascal Poussot 73 Wathan
Pratumwan 58 Ezio Previtali 10 Fazhi Qi 27 Ming Qi 10 Sen Qian 10 Xiaohui Qian
20 Zhen Qian 12 Hao Qiao 10 Zhonghua Qin 23 Shoukang Qiu 66 Muhammad Usman
Rajput 57 Gioacchino Ranucci 20 Neill Raper 57 Alessandra Re 49 Henning Rebber
44 Abdel Rebii 18 Bin Ren 9 Jie Ren 56 Barbara Ricci 51 Markus Robens 44
Mathieu Roche 71 Narongkiat Rodphai 62 Aldo Romani 74 Bedřich Roskovec 51
Christian Roth 28 Xiangdong Ruan 9 Xichao Ruan 73 Saroj Rujirawat 67 Arseniy
Rybnikov 67 Andrey Sadovsky 57 Paolo Saggese 64 Simone Sanfilippo 72 Anut
Sangka 73 Nuanwan Sanguansak 72 Utane Sawangwit 53 Julia Sawatzki 61 Fatma
Sawy 50,48 Michaela Schever 45 Cédric Schwab 53 Konstantin Schweizer 67
Alexandr Selyunin 56 Andrea Serafini 50 Giulio Settanta 47 Mariangela Settimo
35 Zhuang Shao 67 Vladislav Sharov 67 Arina Shaydurova 10 Jingyan Shi 10 Yanan
Shi 67 Vitaly Shutov 68 Andrey Sidorenkov 70 Fedor Šimkovic 61 Chiara
Sirignano 73 Jaruchit Siripak 58 Monica Sisti 42 Maciej Slupecki 20 Mikhail
Smirnov 67 Oleg Smirnov 47 Thiago Sogo-Bezerra 67 Sergey Sokolov 73 Julanan
Songwadhana 72 Boonrucksar Soonthornthum 67 Albert Sotnikov 41 Ondřej Šrámek
73 Warintorn Sreethawong 48 Achim Stahl 60 Luca Stanco 69 Konstantin
Stankevich 70 Dušan Štefánik 52,53 Hans Steiger 48 Jochen Steinmann 54 Tobias
Sterr 53 Matthias Raphael Stock 56 Virginia Strati 69 Alexander Studenikin 11
Shifeng Sun 10 Xilei Sun 22 Yongjie Sun 10 Yongzhao Sun 71 Narumon Suwonjandee
45 Michal Szelezniak 20 Jian Tang 20 Qiang Tang 23 Quan Tang 10 Xiao Tang 54
Alexander Tietzsch 68 Igor Tkachev 41 Tomas Tmej 67 Konstantin Treskov 45
Andrea Triossi 5 Giancarlo Troni 42 Wladyslaw Trzaska 55 Cristina Tuve 68
Nikita Ushakov 51 Johannes van den Boom 51 Stefan van Waasen 47 Guillaume
Vanroyen 10 Nikolaos Vassilopoulos 65 Vadim Vedin 55 Giuseppe Verde 69 Maxim
Vialkov 47 Benoit Viaud 50,48 Moritz Vollbrecht 43 Cristina Volpe 41 Vit
Vorobel 68 Dmitriy Voronin 63 Lucia Votano 5 Pablo Walker 18 Caishen Wang 39
Chung-Hsiang Wang 37 En Wang 21 Guoli Wang 22 Jian Wang 20 Jun Wang 10 Kunyu
Wang 10 Lu Wang 10 Meifen Wang 23 Meng Wang 25 Meng Wang 10 Ruiguang Wang 12
Siguang Wang 27 Wei Wang 20 Wei Wang 10 Wenshuai Wang 16 Xi Wang 20 Xiangyue
Wang 10 Yangfu Wang 10 Yaoguang Wang 13 Yi Wang 24 Yi Wang 10 Yifang Wang 13
Yuanqing Wang 27 Yuman Wang 13 Zhe Wang 10 Zheng Wang 10 Zhimin Wang 13 Zongyi
Wang 66 Muhammad Waqas 72 Apimook Watcharangkool 10 Lianghong Wei 10 Wei Wei
10 Wenlu Wei 18 Yadong Wei 10 Liangjian Wen 48 Christopher Wiebusch 20 Steven
Chan-Fai Wong 49 Bjoern Wonsak 10 Diru Wu 27 Fangliang Wu 25 Qun Wu 10 Zhi Wu
52 Michael Wurm 45 Jacques Wurtz 48 Christian Wysotzki 32 Yufei Xi 17 Dongmei
Xia 17 Xiaochuan Xie 10 Yuguang Xie 10 Zhangquan Xie 10 Zhizhong Xing 13 Benda
Xu 23 Cheng Xu 31,30 Donglian Xu 19 Fanrong Xu 10 Hangkun Xu 10 Jilei Xu 8
Jing Xu 10 Meihang Xu 33 Yin Xu 50,48 Yu Xu 10 Baojun Yan 73 Taylor Yan 10
Wenqi Yan 10 Xiongbo Yan 73 Yupeng Yan 10 Anbo Yang 10 Changgen Yang 28
Chengfeng Yang 10 Huan Yang 37 Jie Yang 18 Lei Yang 10 Xiaoyu Yang 10 Yifan
Yang 2 Yifan Yang 10 Haifeng Yao 66 Zafar Yasin 10 Jiaxuan Ye 10 Mei Ye 31
Ziping Ye 51 Ugur Yegin 47 Frédéric Yermia 10 Peihuai Yi 25 Na Yin 10 Xiangwei
Yin 20 Zhengyun You 10 Boxiang Yu 18 Chiye Yu 33 Chunxu Yu 20 Hongzhao Yu 34
Miao Yu 33 Xianghui Yu 10 Zeyuan Yu 10 Zezhong Yu 10 Chengzhuo Yuan 12 Ying
Yuan 13 Zhenxiong Yuan 34 Ziyi Yuan 20 Baobiao Yue 66 Noman Zafar 51 Andre
Zambanini 67 Vitalii Zavadskyi 10 Shan Zeng 10 Tingxuan Zeng 20 Yuda Zeng 10
Liang Zhan 13 Aiqiang Zhang 30 Feiyang Zhang 10 Guoqing Zhang 10 Haiqiong
Zhang 20 Honghao Zhang 10 Jiawen Zhang 10 Jie Zhang 28 Jin Zhang 21 Jingbo
Zhang 10 Jinnan Zhang 10 Peng Zhang 35 Qingmin Zhang 20 Shiqi Zhang 20 Shu
Zhang 30 Tao Zhang 10 Xiaomei Zhang 10 Xuantong Zhang 25 Xueyao Zhang 10 Yan
Zhang 10 Yinhong Zhang 10 Yiyu Zhang 10 Yongpeng Zhang 30 Yuanyuan Zhang 20
Yumei Zhang 34 Zhenyu Zhang 18 Zhijian Zhang 26 Fengyi Zhao 10 Jie Zhao 20
Rong Zhao 37 Shujun Zhao 10 Tianchi Zhao 19 Dongqin Zheng 18 Hua Zheng 9
Minshan Zheng 14 Yangheng Zheng 19 Weirong Zhong 9 Jing Zhou 10 Li Zhou 22 Nan
Zhou 10 Shun Zhou 10 Tong Zhou 34 Xiang Zhou 20 Jiang Zhu 35 Kangfu Zhu 10
Kejun Zhu 10 Zhihang Zhu 10 Bo Zhuang 10 Honglin Zhuang 13 Liang Zong 10
Jiaheng Zou

###### Abstract

JUNO is a massive liquid scintillator detector with a primary scientific goal
of determining the neutrino mass ordering by studying the oscillated anti-
neutrino flux coming from two nuclear power plants at 53 km distance. The
expected signal anti-neutrino interaction rate is only 60 counts per day
(cpd), therefore a careful control of the background sources due to
radioactivity is critical. In particular, natural radioactivity present in all
materials and in the environment represents a serious issue that could impair
the sensitivity of the experiment if appropriate countermeasures were not
foreseen. In this paper we discuss the background reduction strategies
undertaken by the JUNO collaboration to reduce at minimum the impact of
natural radioactivity. We describe our efforts for an optimized experimental
design, a careful material screening and accurate detector production
handling, and a constant control of the expected results through a meticulous
Monte Carlo simulation program. We show that all these actions should allow us
to keep the background count rate safely below the target value of 10 Hz (i.e.
$\sim$1 cpd accidental background) in the default fiducial volume, above an
energy threshold of 0.7 MeV.

##  1 Introduction

The Jiangmen Underground Neutrino Observatory (JUNO) yellow ; ppnp  is a
multipurpose experiment primarily designed to determine the neutrino mass
ordering and precisely measure the neutrino oscillation parameters by
detecting reactor anti-neutrinos. It is being built in the south of China at
about 53 km distance from the Yangjiang and Taishan nuclear power plants, to
allow the contemporary study of the solar and atmospheric neutrino oscillation
sectors. The large detector volume — 20,000 tons of liquid scintillator (LS) —
and the unprecedented energy resolution of 3% at 1 MeV juno-calibration , make
JUNO the largest LS-based, underground, neutrino observatory, capable of
addressing many important topics in astro-particle physics. The extensive
physics program of JUNO comprises supernova neutrinos, atmospheric neutrinos,
solar neutrinos, and geoneutrinos, as well as new physics searches. A
comprehensive discussion can be found in Refs. yellow ; ppnp .

The main detection channel for reactor anti-neutrinos in JUNO is the inverse
beta decay (IBD) reaction on free protons (see Section 3). Because of its very
low cross-section, only about 60 anti-neutrinos per day will be detected by
JUNO, for a total available thermal power of 26.6 GW${}_{\textrm{th}}$.
Therefore, strict control of radioactive background is mandatory.

The JUNO detector is located in an underground laboratory with 700 m
overburden, i.e. 1800 m.w.e. At this depth, the muon flux at JUNO site is of
about 0.004 Hz/m2 with a mean energy of 207 GeV ppnp . Hence a water Cherenkov
detector is placed around the LS in order to detect and reject the muons. The
rate of muons passing through the ultrapure water is of 10 Hz, while the rate
of muons passing through the LS is of 3.6 Hz. Fast neutrons produced by muons
passing through the rock and the detector materials may reach the LS and mimic
an IBD event: the efficient muon tagging by the veto system of JUNO will
enable removing most of these events leading to a low impact on the background
budget of the fast neutrons. Muons and muon showers interact with 12C in the
LS producing Z$\leq$6 isotopes by hadronic or electromagnetic processes: the
produced $\beta$–n decaying nuclides can also mimic an IBD signal. The
resulted 9Li/8He are the most dangerous correlated background nuclides. In
this respect, there are various physics-driven models for veto strategies to
reduce the impact of the cosmogenic background in the different JUNO physics
channels. Geoneutrinos, produced by 232Th and 238U radioactive decay chains
inside the Earth, are both a background for reactor anti-neutrinos due to the
same interaction channel (IBD) and one of the JUNO physics cases. All these
background sources (cosmogenics and geoneutrinos), which are related to
experimental site location, are discussed elsewhere yellow ; ppnp .

Natural radioactivity exists in all materials and represents a dominant source
of background, which must be reduced with huge efforts on material selection
and environmental control. To reach JUNO sensitivity requirement for the
neutrino mass ordering determination, it is essential to maintain the
background count rate — the so-called _singles_ rate — due to the natural
radioactivity below 10 Hz yellow . In this paper, we will discuss the natural
radioactivity background reduction strategy pursued by JUNO to achieve this
goal. The JUNO detector is described in Section 2. Section 3 details the
natural radioactivity background sources and the anti-neutrino detection
channel. The methodology for background reduction — material screening and
Monte Carlo simulations — is presented in Section 4. Finally, the results are
discussed in Section 5.

##  2 The JUNO detector

Figure 1:  Schematic drawing of the JUNO detector showing the main components
of the experimental ppnp .

The highly demanding performance requested to JUNO in terms of high
statistical precision, excellent energy resolution
($\sim$3%/$\sqrt{(E(\rm{MeV})}$), and low background have driven the
dimensions and design of the experimental setup. It consists of a Central
Detector (CD) and a water Cherenkov detector (WCD) laid in a water pool, and a
muon tracker placed on top of the pool (indicated as Top Tracker, TT), as
shown in Figure 1. In the CD, the huge LS mass – 20 kt – is contained inside a
spherical acrylic vessel with inner diameter of 35.40 m and thickness of 12
cm, for a total mass of about 580 t of acrylic. The LS target has a room
temperature density of 0.86 g/mL and consists of Linear Alkyl Benzene (LAB)
solvent, mixed with 2.5 g/L of PPO (2,5-dyphenyloxazole) as fluor and 3 mg/L
bis-MSB (1,4-bis(2-methylstyryl) benzene) as a wavelength shifter juno-scint .
The vessel is supported by a spherical stainless steel (SS) structure with
inner diameter of 40.1 m, sitting on 30 pairs of SS legs safely rooted to the
concrete floor of the water pool. The anchoring of the acrylic vessel to the
SS truss is ensured by 590 stainless steel rods (SS bars), which end at the
vessel side with hinged connections within acrylic nodes to ensure the
required stress relief. The scintillation light produced by energy depositions
in the LS volume is read by 17,612 20-inch photomultiplier tubes (LPMTs, for
large PMTs) and 25,600 3-inch photomultiplier tubes (SPMTs, for small PMTs),
which are installed on the inner side of the SS truss. The LPMTs are produced
by two different companies: there are 5,000 20-inch dynode photomultipliers
from Hamamatsu Photonics hamamatsu , and 12,612 20-inch microchannel plate
photomultipliers by Northern Night Vision Technology (NNVT) nnvt . All LPMTs
feature a special protection in case of implosion: $\sim$10 mm thick acrylic
semisphere on the top, supported by a $\sim$2 mm thick stainless steel
semisphere on the bottom (left of Figure 2). The SPMTs are produced by HZC
Photonics hzc . The PMTs’ electronics is composed of two parts: the “wet”
electronics is located few meters from the PMTs inside custom made stainless
steel under water boxes (UWBs), while the “dry” electronics is placed in a
dedicated room above the pool. Each UWB contains the customized high voltage
(HV) module, front-end board and readout card for 3 LPMTs or 128 SPMTs. The HV
divider circuit to provide the working voltage to the PMTs is placed at the
back of each phototube inside a customized experimental volume with a
waterproof potting. The cables connecting the PMTs to the UWBs and the UWBs to
the “dry” electronics are kept inside waterproof SS bellows.

Figure 2:  Left: Schematics of an instrumented NNVT photomultiplier. Right:
The acrylic vessel (yellow-green) anchored to the SS truss (light-gray) where
the PMTs are installed and immersed in water (teal). The water pool (dark-gray
– not drawn to scale) has a minimum thickness of 3.9 m from the acrylic vessel
in order to provide a sufficient shielding against the radioactivity of the
rock. The inner part (2.3 m thick) is designed in order to shield the LS from
the radioactivity of the PMTs.

The energy calibration is obtained by a redundant system of multiple sources
(both radioactive and laser-based ones) and multidimensional scan systems,
some of which will be inserted inside the vessel through an acrylic chimney.
Calibration sources of different types and energies can be moved inside the LS
volume or pulled inside a guide tube which surrounds the outside of the
acrylic vessel and runs in a longitudinal loop. Details of the calibration
systems and strategy are reported in Ref. juno-calibration . For the purpose
of this paper, the calibration components that will be permanently mounted in
the CD are the SS cables (with teflon anchors) used to insert the sources, the
Teflon guide tube (with SS cables and sensors), and the ultrasonic sensor
system receivers (which include Teflon, nickel, epoxy, piezoceramics, and
copper) used to reconstruct the exact source positions within the detector.

The entire CD is submerged in a cylindrical water pool with a diameter of 43.5
m and a height of 44.0 m, providing sufficient water thickness in all
directions (at least 3.9 m) to shield the detector from the radioactivity of
the surrounding rock Li_2016 . The inner part of the water pool, with 2.3 m of
water buffer, shields the LS from the radioactivity of the PMTs (right of
Figure 2) with a 1.4 m distance between acrylic vessel and the front face of
the PMT glass bulb. To prevent 222Rn (called also radon or Rn hereafter)
diffusion from the external rocks from dissolving into the water, 5 mm thick
high-density polyethylene (HDPE) panels (liner) are sealing the pool walls and
are sustained by a concrete barrier with a minimum thickness of 70 cm placed
in front of the cavity rock. The water pool is equipped with about 2,400 LPMTs
from NNVT to act as a WCD to veto cosmic muons. The CD and the WCD are
optically separated with Tyvek. The WCD LPMTs are installed on the outside of
the SS truss. Tyvek reflective foils provide a coating for the pool walls and
the SS structure to increase the light collection efficiency. Finally, on the
top of the water pool, a TT is installed to precisely measure the muon
directions and support the veto strategies. It is composed by scintillating
strips decommissioned from the Target Tracker of the OPERA experiment OPERA ,
as described in Ref. JUNO-CDR .

##  3 Signal and radioactivity background

###  3.1 Anti-neutrino signal and selection cuts

JUNO detects electron anti-neutrinos via the IBD reaction,
$\overline{\nu}_{e}+p\rightarrow e^{+}+n$. Here the positron quickly deposits
its kinetic energy and annihilates into two 511 keV $\gamma$-rays, producing
the prompt signal. The neutron, instead, scatters in the detector for tens of
centimeters in the LS until it is thermalized and then captured by a free
proton, with the subsequent release of a 2.2 MeV $\gamma$-ray, which
constitutes the delayed signal (mean capture time $\sim$200 $\mu$s). The
energy deposited by the positron is related to the one carried by the anti-
neutrino with a shift down of about 0.8 MeV. Reactor anti-neutrino energies
typically extend up to about 10 MeV: thus, taking into account the IBD
threshold energy of 1.8 MeV, the deposited energy spectrum for reactor anti-
neutrino analysis is spread between about 1 and 9 MeV.

The IBD reaction shows a peculiar correlation in energy, time and space
between the prompt and delayed signals. Therefore, the picking of anti-
neutrino events is performed by applying, among others, a basic set of
preliminary selection criteria (default cuts) yellow :

  * •

prompt signal energy: 0.7 MeV < $E_{p}$ < 12 MeV;

  * •

delayed signal energy: 1.9 MeV < $E_{d}$ < 2.5 MeV;

  * •

time difference between prompt and delayed signal: $\Delta T_{p-d}$ < 1.0 ms;

  * •

distance between prompt and delayed signal: $D_{p-d}$ < 1.5 m.

On the other hand, the accidental background from natural radioactivity (see
Section 3.2) satisfies the above selection criteria in the same energy range,
and cannot be discriminated by means of further software-based cuts.

The expected accidental coincidence rate $R_{acc}$ caused by natural
radioactivity contaminants can be calculated as

$R_{acc}=R_{p}\cdot R_{d}\cdot\Delta T_{p-d}\cdot\epsilon$ (1)

where $R_{p}$ and $R_{d}$ represent the prompt-like and delayed-like signal
rates. $\epsilon$ is an efficiency parameter that allows a further event
selection by requiring a specific distance Dp-d between the position of
occurrence of the prompt and delayed events within the detector. $\epsilon$
can be derived only by means of Monte Carlo simulations. We optimize $R_{acc}$
with the help of a toy Monte Carlo, and tune both the time and vertex
correlation cuts between prompt and delayed signals according to the ratio of
signal to background (as discussed later in Section 5.3).

Therefore, despite the clear correlations in energy, time and position between
the prompt and delayed signals, it is mandatory to maintain the natural
radioactivity background at extremely low levels. This can be achieved by a
strict reduction strategy during the detector design and construction.

The measurement of solar neutrinos, on the other hand, fully rely on the
detection of their elastic scattering off electrons in the detector medium.
Here only a fraction of the neutrino energy is transferred to the electron and
the resulting electron recoil spectra are continuous even for incident mono-
energetic neutrinos. The lack of a coincidence signature, in this case, poses
even more stringent demands on the background level in the energy region below
about 20 MeV and was already studied in Ref. JUNO-solar .

###  3.2 Natural radioactivity

Natural radioactivity comes from all materials and can only be reduced by
strict requirements on the material screening and environmental control. The
water shielding around the central detector is effective not only for the fast
neutrons generated by muons, but also for the radioactivity from the rock of
the JUNO site. With a deposited energy up to 5 MeV which overlaps with the IBD
energy spectrum, the radioactivity of the materials is one of the main sources
of accidental background. The main contaminants are the following:

  * •

natural long-lived radionuclides 238U and 232Th (with their decay chains
supposed to be at secular equilibrium) and 40K;

  * •

natural medium-lived radionuclides 226Ra, 210Pb/210Bi, 210Po when secular
equilibrium is broken in the 238U chain;

  * •

natural gaseous radionuclide 222Rn;

  * •

anthropogenic radionuclide 60Co.

All of these contaminants can be present in the various materials of the JUNO
detector and may contribute to the singles rate measured in the CD due to the
energy deposition following their radioactive decays. It can correspond either
to the prompt or the delayed event of the IBD reaction depending on their
energy and relative time. The expected IBD rate in the JUNO CD induced by
reactor neutrinos is about 60 counts per day (cpd), while the singles rate
from natural radioactivity should be controlled to less than 10 counts per
second, leading to $\sim$1 cpd accidental coincidence (see Section 5.3) with
default anti-neutrino selection cuts, similar to 8He/9Li and geoneutrinos
background sources. However, for solar neutrino detection whose signal is a
single event, this limit will be several orders of magnitude lower and
achieved by applying more stringent fiducial volume (FV) and timing cuts to
remove natural radioactivity and cosmogenic backgrounds, as described in a
dedicated paper JUNO-solar . We should point out that 14C and 85Kr are not
considered here due to their decay energy (Qβ=156 keV and Qβ=687 keV,
respectively) below the default energy cut for IBD reactions (0.7 MeV –
introduced in Section 3.1). On the other hand, they are a very important
background for low energy solar neutrino studies in JUNO.

The radioactivity from external materials can also be effectively removed by
software-based FV and energy threshold cuts, which can be both optimized based
on the Monte Carlo simulation (see Section 4 and 5). Besides that, it is
important to distinguish the internal background, coming from the LS itself
and the external background, coming from the other parts of the JUNO detector.
Indeed, for the internal background, all the radionuclides especially from the
U/Th chains will contribute to the deposited energy whatever the nature of the
emitted particles ($\alpha$, $\beta$, and $\gamma$) because the energy is
directly released in the sensitive volume. Assuming secular equilibrium, each
of the radionuclides will contribute equally to the singles rate (without
energy cut considerations). In this case, a FV cut would be useless for
removing the background events, since they are uniformly distributed in the
LS. On the contrary, only a part of the radionuclides will contribute to the
external background considering the very short ranges of the $\alpha$ and
$\beta$ particles in solid materials, which will exclude pure $\alpha$ and
$\beta$ emitters. Only high energy $\gamma$s will be able to reach the LS and
deposit energy, mainly coming from 214Bi (1.76 and 2.20 MeV in 238U chain),
208Tl (2.61 MeV in 232Th chain), 40K (1.46 MeV) and 60Co (1.17 and 1.33 MeV).

Radon coming from 238U chain is also a problematic gaseous radionuclide
contributing to both internal and external backgrounds. Before insertion in
the JUNO vessel, the LS may be contaminated during its lifetime (production,
transportation, and storage) with the radon in the air or emanated from the
surrounding materials. As a result, an additional contribution from the long-
lived isotopes 210Pb and its sub-chain 210Bi/210Po has to be considered for
the internal background. As a progenitor of the 214Bi radionuclide, it may
also contribute to the external background if present in the inner water pool.
Finally, ($\alpha$,n) reactions can occur in the LS or in the surrounding
materials due to the U/Th impurities: anyway the impact in singles rate is
expected to be much smaller than the ones just described due to a relative low
cross section.

In the remaining of the paper we will focus on the strategies to reduce the
impact of natural radioactivity on JUNO not to impair the sensitivity for the
neutrino mass ordering determination.

##  4 Methodology for background control

A proper design of the experimental setup is of paramount importance for the
containment of the radioactivity background. The final arrangement of the
detector is a delicate balance between the engineering plan and the need for
the lowest possible count rate due to the spurious events. The trade-off is
usually achieved by a careful selection of the materials to be used for the
construction of the apparatus, according not only to their mechanical
characteristics but also to their intrinsic radiopurity. By means of _ad hoc_
Monte Carlo simulation of the expected radioactivity background, the best
geometrical layout is thus evolved as the result of the radioactivity
screening of the various materials and their positioning within the
experimental setup.

One important point to be underlined for low background experiments is that
very often the sensitivity needed to validate raw materials is at the cutting-
edge of available screening techniques: this difficulty implies that the
approval of certain materials or of particular production and cleaning
protocols requires by itself to conceive non trivial dedicated test
facilities. JUNO surely benefits from the experience of past and running
neutrino and dark matter experiments using the same type of detector, to
select the proper materials and related cleaning procedures to achieve its
goals: the challenge comes from the pushing of ultra–low background techniques
to the largest experimental scale.

In the next sections we will illustrate the experimental techniques used for
the material screening and the simulation codes developed for the evaluation
of the JUNO background.

###  4.1 Material assay and measuring techniques

Material | Mass | Radius | Target impurity concentration
---|---|---|---
238U |  232Th |  40K |  210Pb/222Rn |  60Co
[t] | [m] | [ppb] | [ppb] | [ppb] | [ppb]/[mBq/m3] | [mBq/kg]
Liquid Scintillator |  |  |  |  |  |  |
LS-reactor | 20000 | 0–17.7 | 10-6 | 10-6 | 10-7 | 10-13 ppb |
LS-solar | 10-8 | 10-8 | 10-9 | 10-15 ppb |
Acrylic vessel | 580 | 17.7–17.8 | 0.001 | 0.001 | 0.001 |  |
Acrylic nodes | 28.5 | 17.8-17.9 | 0.001 | 0.001 | 0.001 |  |
Calibration parts | 0.04 |  | 1.5 | 4.5 | 0.02 |  |
SS structure |  |  |  |  |  |  |
– truss | 1000 | 20.0-20.5 | 1 | 3 | 0.2 |  | 20
– bars | 65 | 17.9-20.0 | 0.2 | 0.6 | 0.02 |  | 1.5
LPMT glass |  |  |  |  |  |  |
– NNVT | 84.5 | 19.2-19.8 | 200 | 120 | 4 |  |
– Hamamatsu | 33.5 | 19.2-19.8 | 400 | 400 | 40 |  |
– Veto (NNVT) | 16.0 | 20.2-20.8 | 200 | 120 | 4 |  |
LPMT cover |  |  |  |  |  |  |
– acrylic | 110 | 19.2-19.4 | 0.003 | 0.01 | 0.01 |  |
– SS | 150 | 19.4-19.8 | 0.4 | 2.5 | 0.12 |  |
LPMT readout |  |  |  |  |  |  |
– divider | 0.6 | 19.8-19.9 | 3000 | 5000 | 100 |  |
– potting | 24.5 | 19.7-19.9 | 70 | 50 | 4 |  |
– UWB | 100 | 20.1-20.4 | 50 | 200 | 5 |  | 20
SPMT glass | 2.6 | 19.3-19.4 | 400 | 400 | 200 |  |
SPMT readout |  |  |  |  |  |  |
– divider | 0.15 | 19.4 | 3000 | 10000 | 200 |  |
– potting | 5.1 | 19.4-19.5 | 100 | 50 | 20 |  |
– UWB | 11 | 20.1-20.4 | 50 | 200 | 5 |  | 20
Water | 35000 | 17.8–21.8 |  |  |  | 10 mBq/m3 |
Rock |  |  | 10000 | 30000 | 5000 |  |
Table 1:  Target values for the impurity concentrations in the different
detector materials of the JUNO detector. For each detector component, the mass
and its geometrical position, i.e. the radius quoted from the center of the LS
volume, are reported. LS-reactor refers to the target impurity values required
for mass hierarchy determination, whereas LS-solar refers to the target
impurity values for solar neutrinos studies.

A crucial prerequisite for the components of the setup of ultra-low background
experiments is the radiopurity level. All materials must be selected according
to their intrinsic low concentration of natural radioactive species, and any
processing or handling must be carefully worked out in order not to
accidentally contaminate the bulk or surface of the final product. This is
achieved by setting up a radioactive screening program that includes different
techniques to be exploited at the various stages of the component production
according to the type of contaminant and to the required sensitivity to reach
the JUNO physics goals.

Table 1 reports the minimal requirements for the radiopurity of the materials
to be employed in the JUNO experimental setup. It is worth noting that the
radiopurity requirements are less and less stringent for detector components
with low mass and far from the vessel (from acrylic to SS truss), compared to
the LS itself. A discussion on how these choices were finally made is
postponed to Section 5.1.1. The assumed values are expressed in Bq/kg or in
mass concentration units 222For convenience, the conversions between the two
units used in this paper are reported: 1 mBq of 232Th activity per kg of
material is equal to $2.5\times 10^{-10}$ g/g (or 0.25 ppb) of 232Th mass
concentration in that material. Similarly, 1 mBq/kg of 238U means $8.1\times
10^{-11}$ g/g (or 81 ppt) of 238U, and 1 mBq/kg of 40K means $3.8\times
10^{-12}$ g/g (or 3.8 ppt) of 40K, corresponding to $3.2\times 10^{-8}$ g/g of
natural K. (g/g, i.e. grams of contaminant per gram of material, and its sub-
multiples ppm $=10^{-6}$ g/g, ppb $=10^{-9}$ g/g, and ppt $=10^{-12}$ g/g). In
the following we briefly describe the main measuring techniques available
within the JUNO collaboration which were used to select the materials for the
construction of the experiment according to the requirements of Table 1. For
some of them, e.g. the U/Th content in the LS, dedicated experimental
facilities are being used to screen the samples at the desired sensitivity
that are not described here. A comprehensive discussion of the different
assays and measurement results, in fact, goes beyond the scope of the present
paper. Few details can be found in Section 5.1.1, where references to specific
publications are given.

##### Low background spectroscopy

Low background gamma spectroscopy with High Purity Germanium detectors (HPGe)
is a common technique for screening materials needed to build detectors for
rare event studies. It allows a multi-radionuclide analysis of a sample in one
measurement, giving access to its bulk activity for natural radioactivity (U
and Th chains and 40K), and for cosmogenic or man-made gamma emitters (60Co,
137Cs, etc) in the energy range from 0 to 3 MeV. The main advantage of this
technique consists in measuring independently the activities of several
radionuclides in the U/Th chains in order to check whether secular equilibrium
is achieved. It is of great importance e.g. for the Uranium chain since gamma
spectroscopy is able to quantify 226Ra and 210Pb activities. Low background
gamma spectroscopy with HPGe has typical sensitivities in the 10 ppt – 10 ppb
range or more (100 $\mu$Bq/kg – 100 mBq/kg). Ultra-low background facilities
in underground laboratories may reach the ppt scale ($\sim 10$ $\mu$Bq/kg)
with few tens of kg of sample. Despite a moderate sensitivity compared to the
other techniques described below (NAA and ICP-MS), it is the only technique
able to measure short and medium-lived radioisotopes. The gamma spectrometers
used for material screening in JUNO are HPGe detectors protected by passive
and active shieldings and spread in several underground laboratories around
the world (China JinPing underground Laboratory in China, Laboratoire
Souterrain de Modane in France, Laboratori Nazionali del Gran Sasso in
Italy333Courtesy of Dr. Matthias Laubenstein.) or sea level laboratories (IHEP
in China, Milano-Bicocca in Italy, CENBG Bordeaux in France). They were used
extensively to select a lot of components (such as SS from truss, bars and
nodes, glass from LPMTs and SPMTs, electronics and calibration parts, etc.) or
to investigate for the secular equilibrium break in some of them (acrylic,
PPO, etc.).

##### Neutron Activation Analysis (NAA)

NAA is a very sensitive method for qualitative and quantitative determination
of trace elements based on the measurement of characteristic radiation from
radionuclides formed by neutron irradiation of the material. The principle is
very simple: following neutron capture by the nuclide under investigation, a
product radioactive nuclide is formed which is usually $\beta$-unstable and
decays to excited states of the corresponding daughter nucleus, thus emitting
characteristics $\gamma$-rays that can be measured by a HPGe spectrometer. NAA
can achieve substantially greater sensitivity than direct $\gamma$-ray
counting: it can be applied to quantify the concentration of natural
contaminants (238U, 232Th, and 40K) in detector materials that show no long-
lived neutron activation products emitting $\gamma$ lines which could
interfere with the measurement. The Milano-Bicocca group is being pursuing NAA
on many materials since several years, using the TRIGA Mark II research
reactor of University of Pavia (Italy) as the neutron source and the various
HPGe detectors at the Radioactivity Laboratory of Milano-Bicocca University.
Typical sensitivities are at ppt and sub-ppt levels NAA-talk . For JUNO, NAA
was used for the screening and quality control of acrylic, LAB, Teflon, and
PPO.

##### Inductively coupled plasma mass spectrometry (ICP-MS)

ICP-MS is widely used for screening materials of low background detectors due
to its high sensitivity to trace 238U and 232Th. The ICP-MS located at IHEP in
China is built in a Class 1000 clean room, and all other chemical operations
are done in a Class 100 clean room. The count rate for ppt level of 238U/232Th
can reach $\sim$1000 counts per second (cps), and the detection limit can
reach 0.01 ppt for pure water. For acrylic screening, a vaporization setup for
acrylic pre-treatment is built in a Class 100 environment. With mature
procedures for contamination control, the acrylic samples can be easily
measured by ICP-MS to sub-ppt level in two days sec4:ICP-MS_acrylic . Besides
that, this technique will play an important role on the quality control of the
cleaning procedures and the purified water.

Laser Ablation ICP-MS is a complementary technique to ICP-MS to measure U/Th
contaminations. The chemical preparation of the sample is replaced by a UV
femtosecond laser used in an ablation mode. This promising technique has
preliminarly achieved a surface or bulk sensitivity better than 10-12 g/g
level for U/Th in few minutes with only few tens of $\mu$g sample LA-ICPMS
and is also well-suited to screen the surface treatment of the acrylic panels
and other critical materials for JUNO.

##### Low background radon facilities

There are multiple sources of radon that may contaminate the water or the
nitrogen in JUNO: the contamination of the water or the nitrogen itself, the
radon diffusion through barriers, the radon emanation from materials immersed
in the inner part of the water pool close to the acrylic sphere, etc. Thus,
several facilities have been developed to reduce as much as possible the radon
activity in the water pool or in the nitrogen gas.

The radon measurement system developed at IHEP, which consists of an atomizer,
a de-humidification system and an electrostatic radon detector, can be used to
quantify the radon concentration in the water and gas IHEPradon1 ; IHEPradon2
. The atomizer is a water vapor balancing device which could transfer the
dissolved radon gas from water into air during the water flowing. The
electrostatic radon detector determines the radon concentration by detecting
the alphas from the 222Rn daughters (218Po and 214Po) with a Si-PIN photo-
diode. The sensitivity of the radon detector is $\sim$ 5 mBq/m3 for a one day
measurement. The system will be used to measure and monitor the radon
concentration in the ultra-pure water of the water Cherenkov detector as well
as in the sealed nitrogen gas used on top of the JUNO pool.

A system to measure the radon activity of high purity nitrogen needed by JUNO
was also developed IHEPradon3 . The measurement setup contains two parts, a
radon detection chamber (a 0.28 m3 stainless steel tank) and a radon
enrichment system (with activated carbon as adsorbent). The background of
radon detection chamber is $\sim$ 2 mBq/m3 and the enrichment efficiency of
the system was calculated to be about 50% under 25 slpm flow rate. A radon
activity at the level of 10 $\mu$Bq/m3 could be measured by this system in the
high purity nitrogen.

Low background radon emanation technique is complementary to the low
background gamma spectroscopy technique and allows the measurement of the rate
of radon atoms emanating from the surface of a given material depending on the
activity of 226Ra, its long-lived progenitor. The emanation chamber used at
CENBG laboratory in France is a vast stainless steel tank (0.7 m3) allowing to
fit in large material surfaces or a large number of samples. It is coupled to
a low background electrostatic radon detector equipped with a Si-PIN diode
Radon-detector  to perform the alpha spectroscopy of the 222Rn daughters. The
typical sensitivity of the setup for 1 m2 sample is few hundreds of atoms
emanating per second and per square meter and the ultimate sensitivity can
reach $\sim$ 10 222Rn atoms/s/m2 for a 30 m2 sample Radon-emanation . It is
sensitive enough to screen large volume/surface materials that will be
immersed in or around the JUNO water pool, such as rocks, 20-inch and 3-inch
PMTs, HDPE liner, etc., in order to investigate the fulfillment of the 222Rn
activity requirement in water (see Table 1).

A stainless steel bi-chamber was developed at CPPM laboratory in France to
measure the radon transparency of the liner. The two identical chambers have a
volume of $\sim$1.8 L. The sample to be measured is placed between the two
chambers and the sealing is ensured by two flat Silicone gaskets. The upper
chamber contains a 222Rn source with a concentration of $\sim$ 740 kBq/m3. The
second chamber in which the radon passing through the sample is measured was
initially filled with nitrogen. It is equipped with two valves and a second
circulation pump, to homogenize the gas and fill a commercial 120 cm3 Lucas
cell used as a radon detector with a background of 15 Bq/m3. The 222Rn
transparency down to 2$\cdot$10-5 can be measured by this setup for a material
such as the HDPE liner.

###  4.2 Monte Carlo simulations

A whole detector simulation is realized to evaluate the radioactivity
contributions in terms of singles rate from the main detector materials listed
in Section 2. The geometry of the experimental setup is reconstructed by the
simulation code with highest possible detail. Each component is uniformly
contaminated with the various sources described in Section 3 and its impact on
the background count rate is then computed. The outcome of these simulations
is an invaluable guidance during both the experimental design and the material
selection processes, to choose the best solutions that reduce the impact of
the dangerous background. In the end, the results will be useful for the
evaluation of the overall radioactivity background budget as well as for the
optimization of both the FV and the energy threshold cuts for the data
analysis, depending on the physics channels under study.

The official JUNO offline software is based on the SNiPER framework (described
in Appendix A) and was developed according to the demanding requirements of
the experiment profiting from the modularity and the flexibility offered by
the SNiPER environment sec4:sniper1 ; sec4:sniper2 . It comprises the physics
generator, the detector simulation, and the electronics simulation as
separated modules, which are integrated to return a complete reproduction of
the real events. In the lack of an experimental benchmark while the experiment
is under construction, two additional software codes, ARBY (described in
Appendix B) and G4-LA (described in Appendix C), based on completely different
logical architectures, were used for the simulation of the JUNO background, in
order to have a validation of the background estimation outcomes.

###  4.3 Validation of the Monte Carlo outputs

To validate the results of the JUNO background simulations, both energy
spectra and overall count rates induced by natural radioactivity contaminants
(Section 3.2), as obtained by the three codes, were compared. For the
validation process, the simulated geometry included just the acrylic vessel
and the LS inside it: despite the chosen experimental configuration was quite
simple, these studies involved all relevant physics phenomena and allowed to
highlight unexpected bugs either in the physical implementation or in the
logic of the simulations. For a meaningful comparison, a common set of basic
physical hypotheses was adopted for the three codes: i) the chosen GEANT4
version was 10.04.p2 Geant4-web  with the Livermore low energy electromagnetic
physics list; ii) all codes finally used the Geant4 particle source primary
generator for the properties related to each radioactive decay, i.e. daughter
nucleus, mean life, decay modes, branching ratios, and emission spectra; iii)
from the experience of the Daya Bay experiment DayaBay-scint , which used a
scintillation detector very similar to the JUNO one, the secondary particle
production thresholds in Geant4 were set at 0.1 mm for electrons and at 1 mm
for gammas; iv) in the propagation of protons, alpha particles and nuclear
recoils, the Geant4 StepFunction (the computation of the mean energy loss per
propagation step) was set at default values; v) finally, the scintillation
non-linearity was described by means of the generalized Birks semi-empirical
formula. Here, the energy converted to scintillation photons, $E_{scint}$, is
related to the stopping power $dE/dx$ of a charged particle of kinetic energy
$E$ via:

$E_{scint}=S\int_{0}^{E}{\frac{dE}{1+kB(\frac{dE}{dx})+C(\frac{dE}{dx})^{2}}}$
(2)

with $kB$ and $C$ being the Birks coefficients. The parameter $S$ is a
normalization factor which gives the scintillation efficiency.

| $kB/\rho\times 10^{3}$ | $C/\rho^{2}\times 10^{6}$
---|---|---
Particle type | [g/cm2/MeV] | [(g/cm2/MeV)${}^{2}]$
Electrons, Positrons | 6.5 | 1.5
Alphas, Nuclear recoils | 3.705 | 1.5
Protons | 6.5 | 1.5
Table 2:  Values of the Birks’ coefficients of Equation 2 divided by the LS
density $\rho$ ($\rho=0.86$ g/cm3) used to compute the ionization quenching in
JUNO simulations.

In the Birks model, only a fraction of the energy deposited in the LS by the
ionizing particle is actually transferred to the fluorescent molecule (2.5 g/L
of PPO for JUNO juno-scint ) and ready to be converted in scintillation light,
a fact that is usually referred to as ionization quenching of the light yield.
Table 2 reports the Birks’ coefficients used for the present JUNO simulations,
inherited from Daya Bay experience DayaBay-scint .

For the purpose of the validation of the background simulations, only the
energy deposited and stored in the LS – $E_{scint}$ in Equation 2 – was
considered, i.e. nor optical propagation nor light readout were involved at
this step with SNiPER. The Monte Carlo outputs of the three simulation codes
were thus converted into deposited energy spectra in the range between 0 and
12 MeV with 10 keV binning and without energy threshold applied. Nor detector
time resolution nor energy resolution were included. The whole LS volume —
i.e. scintillator radius $r_{LS}$ equal to the acrylic sphere inner radius,
$r_{LS}$ = 17.7 m, hereafter indicated also as DV (detector volume) — was
initially considered as the detecting medium (no fiducial volume cut applied)
in order to count all energy depositions happening at any place within the
vessel.

The first set of simulations were devoted to the LS contaminants: in this
case, the complete natural decay chains of 232Th and 238U and the sub-chain
starting with 210Pb (following the secular equilibrium break caused by 222Rn
in the 238U chain), as well as the natural contaminant 40K, were considered
separately. For each radioisotope or decay chain progenitor, a total of
$10^{6}$ nuclei were uniformly distributed inside the whole scintillator
volume and the energy depositions following each decay sequence recorded for
the comparative analysis. The resulting summed energy spectra from the four
contaminants obtained by each of the three software codes are superimposed in
Figure 3 (top spectrum) in the energy region up to 5 MeV, where the majority
of the events are distributed. The alpha peak broadening visible in the
spectra is due to the fluctuations of the energy deposited (and consequently
quenched) at each step along the particle track. As one can see, the features
of the spectra are finally in very good agreement. The residual differences
are attributed to the implementation of the quenching calculation, but they
are not relevant for the discussion of the background impact in JUNO. The
position of all quenched $\alpha$ peaks is consistent within 25 keV among the
three codes: this spread has to be compared with the peak broadening due to
step energy deposition fluctuations, of the order of $6-15$ keV depending on
the energy. We have also to note that the energy resolution of the detector
(not included at this stage in the simulations) ranges from about 20 to 30 keV
in the quenched $\alpha$ peak energy interval. Figure 4 shows the quenched
alpha energy as a function of the true energy for the main alpha peaks of both
238U and 232Th chains, as resulting from the SNiPER simulation. The quenching
factor, i.e. the ratio between true and quenched energy, is ranging from 12 to
7 for true alpha energies going from 4 to 9 MeV. Most of the alpha events will
be removed by the default 0.7 MeV energy threshold cut (see later) illustrated
by the dashed line. On Figure 3 (top spectrum), the end-point around 4.8 MeV
is induced by the decay of the 208Tl (Qβ=5.0 MeV). The energy difference is
due to the quenching of the electrons produced in LS after multiple Compton
scattering of the gammas from 208Pb de-excitation.

Figure 3:  Superposition of the sum spectra obtained by considering all energy
depositions in DV following 238U, 232Th, 40K, and 210Pb decays uniformly
distributed within the LS (top) and the acrylic vessel (bottom), as simulated
by each of the three Monte Carlo codes. For any radioactive species, $10^{6}$
($10^{7}$) decays were considered, respectively. In the top plot, the black
and red histograms are overlapping, while the blue one is slightly shifted at
higher energy only for the quenched alpha peaks (see discussion in the text).
Figure 4:  The quenched alpha energy resulting from the SNiPER simulation as a
function of the true energy for the main alpha peaks of both 238U and 232Th
chains is shown as black points. The default 0.7 MeV energy threshold used for
IBD event selection is shown with a dashed line.

For what concerns the acrylic vessel, the same 232Th, 238U, or 40K
contaminants were uniformly distributed within its bulk volume. A total of
$10^{7}$ decays for each of the radionuclides were simulated by the three
codes and the following energy depositions in DV recorded for the comparative
analysis. The obtained results are summarized in Figure 3 (bottom spectrum).
Also in this case the agreement among the three software is extremely good. It
has to be noted that the acrylic vessel simulation results in Figure 3 include
also the contributions of the acrylic nodes and the acrylic chimney.

To convert the number of events collected by the detector into expected count
rates, one has to make hypotheses on the concentration of the residual
impurities in the material of interest. In the case of the components of the
JUNO detector, these inputs are the target values reported in Table 1 and
discussed later in Section 5.1.1.

The count rates generated in DV by the decays of the LS contaminants (LS-
reactor inputs in Table 1) uniformly distributed within the liquid
scintillator, are shown in Figure 5(a) as a function of the energy threshold
$E_{th}$ applied to the spectrum of each simulated contaminant. As one can
see, the resulting rates decrease abruptly as the chosen threshold goes beyond
the energy of the quenched alpha peaks, especially those belonging to the
238U/210Pb chains. For IBD analysis, a good compromise is an energy threshold
of 0.7 MeV, well below the prompt energy of the positron (E${}_{e^{+}}\geq 1$
MeV) and high enough to reduce the accidental rate to an acceptable value that
does not impair the mass ordering sensitivity. This choice of the energy
threshold for the neutrino mass ordering analysis is the default value assumed
by JUNO at this stage, as anticipated for the $E_{p}$ energy selection
criteria in Section 3.1, and will be supposed in all later discussions in this
paper.

(a)

(b)

Figure 5: (a) Expected count rates in DV due to the residual contaminations of
the LS (LS-reactor impurity inputs in Table 1) as a function of the energy
threshold $E_{th}$ applied to the simulated spectra. Solid lines connect the
points to guide the eye. $E_{th}$ = 0.7 MeV is the threshold energy value
chosen for the neutrino mass ordering analysis by JUNO (shown by the dashed
line). (b) Expected background count rates for $E_{th}$ = 0.7 MeV due to
uniform 40K, 232Th, and 238U contaminations in the bulk of the acrylic sphere
as a function of different radius quotes $r_{LS}$ chosen to define the
experimental FV. Solid lines connect the points to guide the eye. Currently,
the default JUNO FV for anti-neutrino analysis is spherical, with radius
$r_{LS}$ = 17.2 m (shown by the dashed line).

The count rates induced in DV by the LS-reactor and the acrylic vessel
impurities (from Table 1) were calculated for the three codes above the
default energy threshold for comparison. The results are reported in Table 3,
separately for each nuclide. The relative uncertainty on the global rates
among the three codes is smaller than 2.5%, both for the LS and the acrylic
simulations.

###  4.4 Comparative impact of material contaminations

The next step after the validation process was to evaluate the impact on the
background rate of the residual impurities contained in all other materials of
the JUNO detector with SNiPER and ARBY tools. Also in this case, the
contaminants were uniformly distributed within the material of interest and
the Monte Carlo codes were set to simulate the decay processes up to the
energy depositions in the LS volume. The results are reported in Table 3 as
count rates in the energy region between 0.7 MeV and 12 MeV in DV, and are
listed separately for the considered contaminant. The achieved agreement
between the two simulation codes is within 7% on all the considered components
of the JUNO setup, and the achieved statistical uncertainty is less than 1%.
The impurity inputs used to derive the contributed rates are the target values
of Table 1. Apart from LS and acrylic, the number of simulated decays for each
radionuclide was 108 for all materials: the only exception was the rock of the
underground laboratory, for which $2\times 10^{10}$ decays were considered for
each of the contaminants. In this case, a hollow cylinder of internal radius
equal to 21.75 m and total height of 43.5 m (i.e. the pool dimensions) was
uniformly contaminated with 238U, 232Th, or 40K inside a thickness of 20 cm
(higher thickness values would make the simulations extremely inefficient due
to self-absorption). The chosen material for the cylinder was SiO2 with a
density close to 2.6 g/cm3, the average density of the rock at the
experimental site. Despite the long simulation times, no events were detected
for 238U and 40K in the scintillator volume, so only upper limits at 90% C.L.
are listed in Table 3 for the background induced by those contaminants in the
laboratory rock. Given the geometrical characteristics and presumed
contaminations of the rock simulation, these results can be considered as
proxies for other materials at similar distances from the detector center,
like the liner covering the rock walls placed in front of the concrete barrier
or the veto detector placed on top of the pool.

Material | Count rates in DV (deposited energy) – $E_{th}$ = 0.7 MeV
---|---
238U [Hz] |  232Th [Hz] |  40K [Hz] |  210Pb [Hz] |  60Co [Hz]
LS-reactor | 0.70 | 0.37 | 0.23 | 0.69 |
Acrylic | 1.74 | 0.74 | 4.65 |  |
Calibration parts | 0.5 | 0.6 | 0.2 |  |
SS structure |  |  |  |  |
– truss | 0.02 | 0.08 | 0.002 |  | 0.01
– bars | 3.6 | 4.8 | 1.0 |  | 4.3
LPMT glass |  |  |  |  |
– NNVT | 6.04 | 4.03 | 0.17 |  |
– Hamamatsu | 4.79 | 5.33 | 0.69 |  |
– Veto (NNVT) | 0.17 | 0.14 | 0.003 |  |
LPMT cover |  |  |  |  |
– acrylic | 0.0001 | 0.0004 | 0.0006 |  |
– SS | 0.01 | 0.08 | 0.004 |  |
LPMT readout |  |  |  |  |
– divider | 0.10 | 0.23 | 0.004 |  |
– potting | 0.11 | 0.11 | 0.007 |  |
– UWB | 0.21 | 1.2 | 0.02 |  | 0.003
SPMT glass | 0.24 | 0.30 | 0.21 |  |
SPMT readout |  |  |  |  |
– divider | 0.08 | 0.37 | 0.009 |  |
– potting | 0.09 | 0.06 | 0.03 |  |
– UWB | 0.03 | 0.16 | 0.002 |  | $4\times 10^{-4}$
Water (222Rn) | 0.77† |  |  |  |
Rock | < 0.01‡ | 0.05 | < 0.09‡ |  |
Table 3:  Background count rates in detector volume (DV) in the energy range
between 0.7 MeV and 12 MeV induced by the various detector components with the
contaminant concentrations of Table 1. The results are given for each
contaminant separately, for a complete picture. Only the deposited energy is
considered in these results, i.e. no optical propagation nor detector energy
resolution were applied.
†This simulation refers to a uniform contamination of 10 mBq/m3 of 222Rn.
‡Upper limits at 90% C.L.

Concerning 222Rn distributed in the water of the pool, the simulation was
performed considering only the inner water pool with a water shell of 2.3 m
thickness placed at a radius of 17.8 m from the detector center: this choice
allows to attain reasonable computation times and is justified by the fact
that this layer is the closest to the CD.

The “node and bar” simulation was performed considering just the stainless
steel components incorporated in the nodes at the acrylic vessel side and the
stainless steel bars connecting the truss and the acrylic sphere, since the
acrylic material making the nodes is much cleaner than the stainless steel and
its contribution is already included in the results of the vessel. The
outcomes are reported under the SS bars entry in Table 3. For the calibration
system, all components that are permanently mounted on the CD (described in
Section 2) were simulated separately and the results added together in Table
3; the contribution from the calibration sources (permanently stored in the
calibration house on top of the detector) is found to be negligible. For the
LPMTs, the contributions of the radioactivity of the glass and of the
protection cover are reported as independent entries. Moreover, given the
different impurity concentration inputs for Hamamatsu and NNVT PMTs or the
different positions within the setup of the PMTs (e.g. veto PMTs with respect
to “signal” PMTs), the glass contributions for the different LPMTs are listed
separately. Finally, the signal readout simulations for both the LPMT and the
SPMT systems were done separately for the components sitting on the back of
the photomultipliers (divider and potting) and for the electronics boards
placed in the under water boxes (UWBs) ppnp .

Despite the assumed impurity level in the various components reported in Table
1 are among the smallest achievable by available techniques, the overall
expected background count rate in Table 3 is quite significant. The only means
to reduce it is to shield the external sources by applying a software-based FV
cut. Figure 5(b) shows how effective is the rate reduction with increasing
thickness of the external “dead layer” in the LS volume in the case of the
background induced by the acrylic sphere contaminants (the closest to the
detector FV). At this stage, the “dead layer” thickness chosen for IBD
analysis by the JUNO collaboration is 50 cm, which reduces the count rate by
about one order of magnitude. Therefore, in the remaining of the paper a
spherical FV with radius $r_{LS}$ = 17.2 m will be considered as the default
value.

##  5 Results of the Monte Carlo simulations

In this last part of the paper we discuss the outcomes of the background Monte
Carlo simulations performed with the SNiPER software after enabling the full
reproduction of the event formation, i.e. taking into account all physical and
geometrical effects that come into play following the energy deposition in the
LS. Therefore, in this section all results include the energy resolution, the
optical propagation, the charge conversion, and the non-uniformity response of
the detector (these effects were deeply described in previous works juno-
calibration ; DayaBay-scint ).

The assumed impurities for the main components of the JUNO detector, which are
the inputs to compute the expected background impact, are first analysed in
Section 5.1. The simulation outcomes are then reported in Section 5.2 in the
chosen energy interval between 0.7 MeV and 12 MeV. Finally, the different
possible scenarios resulting from the accidental coincidences that could
resemble an IBD event are discussed in Section 5.3.

###  5.1 Target impurity concentrations in the JUNO detector and environment

Table 1 reports the assumed upper limits for the concentration of the natural
contaminants in the materials used for the JUNO detector. They should be
regarded as the target values that the JUNO collaboration has set for the
expected activities due to the radioactive impurities in the detector
components: some of those values are based on preliminary measurements of the
final products (acrylic, SS structure, PMT glass, components of calibration
system), others are derived from the literature or from the experience of
similar experiments. The motivations for those choices are given in Section
5.1.1.

Special attention must be devoted to the measures implemented to keep the
probability of environmental contamination as low as possible, in particular
during the detector installation which represents a delicate phase where the
risk of nullifying all previous efforts is particularly high. This is
discussed in Section 5.1.2.

####  5.1.1 Radioactivity inputs for the detector components

The LS is the key component of the JUNO detector. The residual contaminations
of Table 1 in the case of IBD analysis are assumed based on the experience of
the Borexino Borexino  and KamLAND KamLAND  experiments. JUNO LS will undergo
a sophisticated purification procedure that foresees four steps: Al2O3
filtration column (to improve optical properties), distillation (to remove
heavy metals and improve the transparency), water extraction (to remove
radioisotopes from U/Th chains and 40K), and steam stripping (to remove
gaseous impurities, such as Kr and Rn). Preliminary tests performed with
prototype plants indicated that those assumptions for 238U and 232Th (LS-
reactor inputs of Table 1) are within reach, so they are taken as the
reference radiopurity requirements for the neutrino mass ordering physics
channel. The OSIRIS (Online Scintillator Internal Radioactivity Investigation
System) quality-check detector osiris , which will be placed just before the
LS injection point into the acrylic vessel, is designed to assure the
fulfilment of the 10-15 g/g requirements for 238U and 232Th via a real-time
measurement before filling the detector. The huge dimensions of JUNO required
careful planning of the LS purification plants to correctly size them
according to the unprecedented LS volume that will be processed. Additional
key points of the plant design are severe 222Rn tightness requirements for all
pipes (see Section 5.1.2), strict demands on the quality of the water (232Th
and 238U $\leq 10^{-16}$ g/g, 222Rn $\leq 10$ mBq/m3) provided to all
purification stages by the ultra pure water system that will be installed in
the laboratory, and heavy prerequisites for the quality of the pure N2 (222Rn
$\sim 10~{}\mu$Bq/m3) that will be supplied by the nitrogen system to the
purification plants and the LS containers, including the water pool. Moreover,
strict cleaning requirements are foreseen for all mechanical components of the
purification plants as well as severe dust control during the production and
the installation. A constant check of the achievements during the installation
phase will be pursued by means of the OSIRIS detector and of _ad hoc_
laboratory tests exploiting NAA coupled to coincident $\beta-\gamma$
measurements. All these precautions will hopefully grant the LS-solar
contamination assumptions of Table 1. Once again, all these requirements
represent challenges that were never faced before the JUNO experiment, since
the unprecedented LS mass prevents from reiterating the purification steps
after the detector filling.

The aims in terms of radiopurity of the acrylic vessel are very similar to
those achieved by the SNO solar neutrino experiment SNO . The chosen
production company proved to have the expertise and the proper facilities to
sustain the production and assembly of the huge acrylic panels needed to build
the JUNO sphere: a dedicated Class 10,000 clean room was set up at the company
site, the mold is cleaned with deionized water, all pipes and containers are
carefully washed, and 222Rn concentration is constantly monitored in all work
areas. After the final grinding and polishing processes, a thin protection
film made of polyethylene (PE) will be placed on each acrylic panel as UV
protection and to prevent 222Rn diffusion and dust deposition on the surfaces:
the film will be removed as last step during the vessel installation at the
experimental site (see Section 5.1.2). All production stages are performed
under the strict supervision of the JUNO collaboration, to guarantee high
quality optical properties and negligible radioactive contamination during all
phases of the sphere construction. Preliminary measurements of the bulk
radioactivity of the produced acrylic panels show that the limits reported in
Table 1 are safely within reach. A severe control of the mass production is
being pursued by means of NAA and ICP-MS. The residual contaminants on the
acrylic surfaces is being monitored by LA-ICPMS. Gamma spectroscopy on random
samples will also be performed profiting of the most sensitive HPGe detectors
to confirm that no contamination out of secular equilibrium, in particular for
the 238U chain, occurred during the panel production.

The structure that sustains the acrylic vessel is entirely made of SS. More
than 1 kton of low radioactivity SS – grade 304L – is produced by smelting of
the molten iron in a clean furnace; no scrap steel is used. Also in this case,
the whole production process is performed under the supervision of the JUNO
collaboration. From the background point of view, the most critical components
of this structure are the SS bars used to anchor the acrylic vessel to the
truss itself. In fact, those rods will be embedded in the vessel through the
acrylic node (see Figures 11(b) and 12(c)), therefore they will be placed very
close to the detector volume. For this reason, the requirements for the
radiopurity of the SS bars in Table 1 are about one order of magnitude more
severe than the ones for the SS truss. Preliminary gamma spectroscopy
measurements confirmed the good quality of the purchased SS. Radioactivity
checks will be performed during the production process to confirm the
compliance with the upper limits of Table 1.

The other critical component for the JUNO background budget is the PMT glass.
Table 1 details the agreed maximum impurity concentrations reported in the
contracts signed with the PMT vendors. A huge R&D effort was put by the NNVT
company, in close cooperation with the JUNO collaboration, into controlling
all glass production steps to reach the subscribed limits sec5:MCP . Gamma
spectroscopy measurements on few glass bulb specimens were also performed with
different HPGe detectors to verify compliance of both Hamamatsu LPMT glass and
HZC Photonics SPMT glass cao2021mass  with the impurity concentration
requirements during the mass production.

Concerning the 222Rn in water, the requirement of 10 mBq/m3 in Table 1 is
based on the experience from SuperKamiokande and SNO experiments Nakano_2020 ;
Blevis_2004 . This is quite challenging because of the radioactivity of the
rock at the JUNO site (10 ppm for 238U/226Ra) and the high radon level
expected in the environment. A prototype of the water purification system
demonstrated the ability to decrease the Rn in water below 10 mBq/m3 by using
degassers and a microbubble generator. During the data taking, the ultrapure
water will continuously circulate at a flow of 100 tons/h from the
purification system to the top and bottom inlets of the inner water pool
towards the outer water pool. This strategy will prevent a possible loading of
Rn in water when circulating near the pool edges, where the Rn activity is
expected to be higher than at the center. To prevent the Rn diffusion from the
rock and the concrete to the pool, a HDPE liner will be installed on the walls
of the water pool. Preliminary measurements of its transparency performed in
laboratory have demonstrated a sufficient Rn suppression factor by more than 4
orders of magnitude using HDPE samples with 5 mm thickness. In addition, the
combination of a N2 blanket (purified at 1 mBq/m3 Rn level) in overpressure
and a thick Tyvek cover on top of the pool will isolate the water from the air
of the underground laboratory. The radon in the inner water pool may also come
from the Rn emanation of the detector components in contact with the inner
water pool, especially the PMT glass, the dominant material considering its
radioactivity: several specimens from the three types of bare PMTs (NNVT,
Hamamatsu, and HZC) were screened by means of the Rn emanation facility
described in Section 4.1 and have shown a negligible contribution compared to
the 10 mBq/m3 target activity. Some efforts have still to be done on 226Ra
removal in water with resins. All these strategies and R&D efforts are
converging in order to fulfill the Rn requirement of Table 1, but cannot
guarantee it regarding possible unexpected variation of the Rn level during
JUNO data taking. Anyway, there are some margins and an increase of the Rn
activity, for example, by a factor of 5 in the inner water pool would imply an
increase of the count rate in the FV within 0.3 Hz, i.e. well below other
contributions.

####  5.1.2 Environmental contamination control

Even though the selected materials for the detector are compliant with the
JUNO requirements, they can be contaminated afterwards by the environment
during production, transportation, installation, as well as during data
taking. As a result, special protections and surface cleaning are essential
for all the critical materials after their final installation in the detector
and before filling the detector with ultra pure water and LS.

Special attention must be devoted to the surface cleanliness of the tanks or
the equipment that will be directly in contact with the LS during the
purification steps. The baseline limit for the residual dust on the surface is
0.1 mg/m2 assuming that the radioactivity in the dust is similar to the one of
the rock (see Table 1): in this way the resulting contamination should not
exceed the 10-16 g/g level in 20 kt of LS.

The cleanliness of the environment during installation and vessel filling is
also very important, since the acrylic will be exposed to the air for a
limited time after the thin PE film removal and subsequent final cleaning, and
before the filling with the LS. Our baseline for the cleanliness of the air
inside the acrylic sphere should reach a level better than Class 10000.

The radon concentration in the air of the underground laboratory can reach
more than 100 Bq/m3 with a risk of 222Rn to be dissolved in the LS, leading to
the long-lived 210Pb radionuclide. It is thus very important to have stringent
leakage requirements. The leakage test is planned by means of vacuum force
technology. Assuming the pressure differential to be 1 bar and the radon
concentration in underground air equal to 100 Bq/m3, the leakage for the
single component should reach $\sim$4$\times$10-6 mbar$\cdot$L/s with vacuum-
air test. The amount of dust and rock that may remain in the water pool should
be minimized by a strict quality control of the surfaces before filling the
detector with ultrapure water. Details concerning the procedures of
cleanliness and leakage control are still under finalization and will be
presented in forthcoming JUNO technical papers.

###  5.2 Expected background count rates in JUNO

Full event simulation is performed with the SNiPER framework, including the
energy resolution, the optical propagation, the charge conversion, and the
non-uniformity of the detector response. The radioactivity coming from the
main materials are simulated with the same statistics presented in Section
4.2, and the total number of photoelectrons (PEs) collected by both LPMTs and
SPMTs is evaluated for each event. After the energy conversion, the obtained
spectra of the reconstructed energy $E_{rec}$ are analysed to derive all
needed information. The conversion from the total number of PEs to the energy
unit (MeV) is carried out by simulating the uniformly distributed 1 MeV gammas
depositing energy in the whole LS volume. The energy non-linearity of the
detector response is out of the scope of this paper (see Ref. juno-calibration
for details) and thus a single energy value is sufficient to derive the
reconstructed energy. The resulting total number of PEs as a function of
volume is shown in Figure 6, with $\sim$1400 PEs expected at the center of the
detector. The sharp decrease of the number of PEs at large radii is due to the
energy leakage near the edge (Compton scattering) and total reflection of the
optical photons at the interface between the acrylic and water, which will
lead to larger statistical fluctuations on the reconstructed energy. Combining
this effect with the energy resolution, this explains why the reconstructed
energy from natural radioactivity can be spread up to 6 MeV (see later Figure
8).

Figure 6:  Uniformly distributed 1 MeV gammas are simulated in the LS, and the
total number of PEs from both LPMTs and SPMTs as a function of volume is shown
in this figure.

The impurity of each material is taken from Table 1 and the final SNiPER
simulation results, including all above mentioned effects, as a function of
energy threshold $E_{th}$ and FV radius are shown in Figure 7. The total count
rate expected for JUNO above $E_{th}$ = 0.7 MeV is about 60 Hz as shown in
Figure 7(a). About 70% of the alpha events in the LS are removed after
applying the $E_{th}$ cut due to their lower quenched energy. Additional alpha
event identification and rejection are foreseen with an efficiency larger than
99% Keeffe2011  during data taking by using well-known pulse shape
discrimination techniques. The total singles rate with energy larger than 0.7
MeV as a function of FV cut is illustrated in Figure 7(b), where a sharp
decrease of the rate from the edge of the LS to the center is clearly visible.
Thus, the external background can be effectively removed with a suitable FV
cut, as anticipated by Figure 5(b).

(a)

(b)

Figure 7: Full background simulation results with SNiPER including all materials composing the detector with the contaminations listed in Table 1. (a) Singles event rate as a function of energy threshold in DV. About 60 Hz total singles rate is achieved with 0.7 MeV energy threshold (shown as dotted line). (b) Singles event rate with energy larger than 0.7 MeV as a function of the FV cut. The singles rate can be reduced down to 7 Hz within a radius of 17.2 m (shown as dotted line). Material | Mass | Target impurity concentration | Singles
---|---|---|---
238U |  232Th |  40K |  210Pb |  60Co | DV | FV
| [t] | [ppb] | [ppb] | [ppb] | [ppb] | [mBq/kg] | [Hz] | [Hz]
LS-reactor | 20000 | 10-6 | 10-6 | 10-7 | 10-13 |  | 2.5 | 2.2
Acrylic | 610 | 10-3 | 10-3 | 10-3 |  |  | 8.4 | 0.4
SS structure | 1000 | 1 | 3 | 0.2 |  | 20 | 15.9 | 1.1
65 | 0.2 | 0.6 | 0.02 |  | 1.5
PMT glass | 33.5 | 400 | 400 | 40 |  |  | 26.2 | 2.8
100.5 | 200 | 120 | 4 |  |
2.6 | 400 | 400 | 200 |  |
PMT readout | 125 | 68 | 194 | 5 |  | 16 | 3.4 | 0.4
16.3 | 93 | 243 | 12 |  | 14
Other |  |  |  |  |  |  | 2.5 | 0.3
Sum | 59 | 7.2
Table 4: Final background budget for the main materials used in the JUNO
detector with reconstructed energy $E_{rec}$ larger than 0.7 MeV. The expected
count rates are given both in the full DV ($r_{LS}$ = 17.7 m) and in the
default FV ($r_{LS}$ = 17.2 m). The “Other” components include all materials
that have relatively smaller contribution to the background (compare with
Table 3), such as the calibration parts, the LPMT cover, the rock, and the
radon in water. These results include energy resolution, optical propagation,
charge reconstruction, and non-uniformity corrections.

Table 4 summarizes the contaminations and count rates of each material. The
mass and the putative contaminations for each material are copied here from
Table 1 for convenience. Count rates are reported in the case of both DV
($r_{LS}$ = 17.7 m) and the default FV cut ($r_{LS}$ = 17.2 m). Compared with
Table 3, the final count rates in DV are larger after applying the energy
resolution, especially the worse energy resolution at the edge. Based on the
results of Table 3, we list in Table 4 only those items giving the dominant
contributions (above 0.3 Hz in the FV), while materials with small
contributions are grouped together in “Other”. In the last row of the table,
the expected sum count rates from all material contaminants are shown: the
main contributions are due to the glass of the PMTs, the SS structure, and the
LS itself. It has to be stressed once more that the impurity concentrations of
the various detector components of Table 1 are presumed on the basis of
preliminary screening measurements or of literature values, and should be
updated once the final numbers will be available. In any case, the JUNO
requirement for a background count rate lower than 10 Hz for the IBD analysis
channel seems within reach.

The informations contained in Figure 7 can be combined in a single plot, which
better shows the correlations among the different parameters. The two
dimension distribution of singles rate as a function of volume (bottom X-axis)
or radius (top X-axis) and reconstructed energy Erec (Y-axis) is shown in
Figure 8. The contribution of the LS is clearly identified by its uniform
distribution in the volume up to $\sim$1 MeV. Most of the events come from the
external background (i.e. from the materials outside the LS) and can be
effectively removed by the FV cut at rLS=17.2 m illustrated by the dashed
line.

Figure 8:  The singles event rate as a function of volume (bottom X-axis) or
radius (top X-axis) and reconstructed energy Erec (Y-axis). The default FV
radius cut is shown as the dashed line.

###  5.3 Accidental coincidences from natural radioactivity

Figure 9:  The integral distribution of the suppression factor (1/$\epsilon$)
as a function of the distance Dp-d for a time coincidence window $\Delta
T_{p-d}$ = 1 ms between the prompt and the delayed signals with energy in the
ranges $E_{p}$ = [0.7, 12] MeV and $E_{d}$ = [1.9, 2.5] MeV, respectively. The
default Dp-d range (i.e. Dp-d = [0, 1.5] m) is shown as the dashed line.

As anticipated in Section 3.1, we optimize the expected accidental coincidence
rate $R_{acc}$ (see Equation 1) with the help of a toy Monte Carlo and using
the default selection cuts, as described in the following. One pair of events
(prompt and delayed) in the chosen energy intervals ($E_{p}$ = [0.7, 12] MeV
and $E_{d}$ = [1.9, 2.5] MeV) are randomly sampled 109 times from the
distribution of Figure 8. At each selected energy, the associated frequency
(rate) will depend on the position (radius) within the detector. The
geometrical distance between each pair of prompt and delayed events can be
calculated by converting the spherical coordinates to polar coordinates. By
choosing a time coincidence window between the prompt and delayed signals
($\Delta T_{p-d}$ < 1 ms), related to the neutron lifetime in the IBD
reaction, the $R_{p}\cdot R_{d}\cdot\Delta T_{p-d}$ distribution as a function
of the distance Dp-d can be calculated. Therefore, the selection of a specific
Dp-d range of distances between the prompt and the delayed events allows a
further reduction of the accidental rate by the associated suppression factor
(1/$\epsilon$), plotted in Figure 9. The suppression factor decreases sharply
at large Dp-d distances, and this parameter can be tuned together with the
time coincidence window $\Delta T_{p-d}$ to optimize the IBD selection
criteria according to the actual experimental conditions.

Table 5 shows an example of possible tuning of the resulting accidental rate
$R_{acc}$ by different choices of the FV cut and of the threshold energy
$E_{th}$, with $\Delta T_{p-d}$ window and Dp-d range fixed at 1 ms and 1.5 m,
respectively. The final goal is to achieve the most favorable IBD signal to
background ratio for the neutrino mass ordering analysis.

Figure 10:  The differential (integral) distribution of the accidental rate as
a function of radius with the default selection cuts of Section 3.1 is shown
as the red (blue) histogram. The default FV radius cut is shown as the dashed
line.

The accidental rate $R_{acc}$ as a function of the volume (bottom X-axis) or
radius (top X-axis) with default selection cuts and with the impurity inputs
of Table 1 is shown as the red histogram in Figure 10, with the dominating
contribution coming from the external contaminations. The integral rate of the
red histogram (i.e. total $R_{acc}$ within the chosen FV) is shown as the blue
histogram in Figure 10.

Racc [cpd] | Fiducial volume radius [m]
---|---
17.0 | 17.1 | 17.2 | 17.3 | 17.4
$E_{th}$ = 0.7 MeV | 0.20 | 0.41 | 0.89 | 2.0 | 4.9
$E_{th}$ = 0.8 MeV | 0.19 | 0.38 | 0.83 | 1.9 | 4.6
$E_{th}$ = 0.9 MeV | 0.17 | 0.35 | 0.78 | 1.8 | 4.3
Table 5:  Evolution of the accidental rate $R_{acc}$ with FV and energy
threshold cuts. An accidental background rate of 0.89 cpd is expected with
default cut values $E_{th}$ = 0.7 MeV and FV with $r_{LS}$ = 17.2 m, and with
$\Delta T_{p-d}$ coincindence window and Dp-d upper distance fixed at 1 ms and
1.5 m, respectively.

The expected accidental background rate due to natural radioactivity is
expected to be about 0.9 cpd with default cuts. As Table 5 shows, this rate is
very sensitive to the selection of the FV radius with more than a factor 2 of
increase or decrease when varying the FV cut by 0.1 m around 17.2 m. From
Figure 10, the accidental background is definitely negligible for $r<$ 16 m.
Finally, with conservative assumptions, the accidental background rate from
natural radioactivity is expected to be lower than the two other main
backgrounds, i.e. the cosmogenic background ($\sim$1.6 cpd) and the
geoneutrino background ($\sim$1.1 cpd), even without the muon veto ppnp .

##  6 Conclusion

JUNO is a 20 kt LS detector whose primary goal is to determine the neutrino
mass ordering by detecting reactor anti-neutrino from two nuclear power
plants. With only 60 IBD events per day in the 0-10 MeV energy range, the
control of the background sources is crucial. Apart from cosmogenic and
geoneutrinos sources, the natural radioactivity of the materials is one of the
key background with a deposited energy in the detector up to 5 MeV,
overlapping with the anti-neutrino signal. This paper summarized the JUNO
strategy in order to contain the count rate due to natural radioactivity at a
level of 10 Hz in order to minimize its impact on neutrino mass ordering
determination. Besides the optimization of the JUNO design, Monte Carlo
simulations and material screening are being conducted to achieve the best
radiopurity of the detector. Monte Carlo simulations were performed using
three different codes (SNiPER, ARBY and G4-LA) in order to cross-check the
results of the JUNO offline software, SNiPER, regarding the deposited energy
and the induced singles rate in the full volume. An agreement within 2.5% for
the count rate was achieved for LS and acrylic vessel simulations, validating
the physics inputs of SNiPER. After including the energy resolution, the
optical propagation, the charge conversion, and the non-uniformity response of
the detector in the full SNiPER simulation (benefiting from the Daya Bay
experience), the count rate for each component (LS, acrylic vessel, SS
structure, PMT glass and readout, radon in water, calibration system, rock,
etc.) was derived assuming default cuts (0.7 MeV energy threshold and 17.2 m
FV radius) and JUNO target radiopurity requirements. These impurity
concentration requirements, usually at the cutting-edge of available
techniques, are based both on the experience of the previous experiments
(especially for LS and radon in water), and on screening measurements
performed on pre-production or production samples (acrylic, SS structure, PMT
systems, calibration parts) using several existing or developed on purpose
screening facilities within the JUNO collaboration. The results of the full
simulation showed that the total expected count rate in JUNO is $\sim$7 Hz,
i.e. compliant with the 10 Hz target for the IBD analysis channel, with three
main contributions from LS, SS structure, and PMT glass. Of course, the
singles rate will be updated once the final numbers will be available. Based
on a toy Monte Carlo using the count rate from natural radioactivity as well
as time coincidence window of 1 ms and distance cut of 1.5 m for two singles
events, an accidental background rate of $\sim$0.9 cpd was derived. It
demonstrates that this background rate is much lower than the IBD signal
($\sim$60 cpd) but also lower than the two other main background sources, i.e.
cosmogenics ($\sim$1.6 cpd) and geoneutrinos ($\sim$1.1 cpd). There is room
for improvement during the running phase of JUNO where the default cuts,
including energy threshold, fiducial volume radius, time coincidence and
distance cut, will be tuned according to the actual background count rate to
optimize the signal over background ratio, in order to increase the exposure
to the best of our ability for neutrino mass ordering determination. The
stricter requirements on the radiopurity of the LS (LS-solar) will make it
possible to study solar neutrinos with the JUNO experiment, with the external
background made totally negligible by the application of more stringent
fiducial volume cuts JUNO-solar .

##  Appendix A SNiPER simulation framework

(a)

(b)

(c)

(d)

Figure 11: Details of the JUNO experimental setup as reconstructed by the
SNiPER simulation code: a) SS truss supporting the PMTs and the acrylic
sphere; b) Acrylic sphere containing the LS with bars on the outside for the
connection to the steel truss; c) Chimney between the central detector and the
calibration house on the top; d) Large and Small PMT systems.

The JUNO offline software is developed using the SNiPER (Software for Non-
collider Physics ExpeRiments) tool sec4:sniper1 ; sec4:sniper2 . The
simulation framework is in charge of managing event data, detector geometries
and materials kli_2018 ; szhang_2021 , physics processes, simulation truth
information, etc. It glues physics generator, detector simulation and
electronics simulation modules together to achieve a full simulation chain.

For the Geant4-based detector simulation, the detector geometry includes all
main materials, such as LS, acrylic sphere, SS structure, PMTs, water pool,
and rock, and the visualization geometries You_2018 ; Zhu_2019  are shown in
Figure 11. The physics generators generate kinematic information of primary
particles, which are saved into GenEvent objects. In the next step, the
detector simulation algorithm accesses these GenEvent objects and starts
tracking. Hits, which contain charge and time information, are generated in
sensitive detectors and saved in SimEvent objects. The main parameters for PMT
response are set to test data, which makes the results close to the real data
in future. After that, the electronics simulation algorithm reads these
SimEvent objects and performs the digitization, which generates ElecEvent
objects containing waveforms information. These waveforms are processed by PMT
calibration algorithm and CalibEvent objects are saved. The event
reconstruction algorithm lizy_2021 ; qianz_2021 ; huang_2021  performs the
event reconstruction by reading CalibEvent objects and stores RecEvent
objects. At last, physicists can perform any physics analysis from RecEvent
objects.

In this paper, only optical simulation is done considering the quenching
effect, absorption, re-emission etc. Most of the optical parameters are got
from Daya Bay, and scaled to JUNO expected number. In the case of radioactive
decay chain simulations (e.g. 238U, 232Th), each daughter nucleus is simulated
separately without secondary decay. At the end, all events are added together
in a single spectrum assuming the chain is in secular equilibrium.

##  Appendix B ARBY simulation code

(a)

(b)

(c)

(d)

(e)

Figure 12: Details of the JUNO experimental setup as reconstructed by the ARBY
simulation code: a) SS truss supporting the PMTs and the acrylic sphere; b)
Acrylic sphere containing the LS with bars on the outside for the connection
to the steel truss; c) Acrylic node and supporting SS bar; d) Large and Small
PMT systems; e) Sections of the LPMTs showing potting elements.

ARBY is a general purpose Monte Carlo simulation code — written by O.
Cremonesi at INFN Milano-Bicocca — designed for low energy particle physics
applications. It is based on GEANT4 toolkit Geant4  and its main peculiarities
are its high flexibility and ease of use. In ARBY, in fact, the geometrical
description of the particular application to be simulated by the user (with
very simple and intuitive commands) is completely separated from the physics
implementation and code evolution, which are maintained by the developers.
ARBY covers a wide variety of GEANT4 physics processes, includes the
propagation of photons, alpha and beta particles, muons, neutrons and protons
as well as nuclear recoils from decaying nuclides, and allows the generation
of radioactive decay chains. For the latter, ARBY uses the G4RadioactiveDecay
class of GEANT4, which offers the possibility of simulating the full
disintegration of a nuclide through different processes profiting from an
extensive data base (ENSDF ensdf ) to obtain all relevant information about
the decay. When a daughter nucleus is itself unstable, like in the natural
decay chains of 238U and 232Th, ARBY allows to keep track of the single steps
in the chain — i.e. of each individual decay — while scoring the event
information. In this way the peculiarities of subsequent decays, like time
correlations, do not get lost and allow to reconstruct particular effects
which can manifest experimentally. Moreover, atomic effects (like X-ray
production) and nuclear recoils accompanying each decay are also considered by
ARBY, and included in the stored data. Other information like the position of
the event in the detector volume or the particle quenching in organic
scintillators can be also saved for each event. The user needs just to
describe the detector arrangement by means of a configuration file, where
details concerning the materials in use as well as the position of the
radioactive sources — i.e. the contaminant concentration of the different
materials — are declared.

The ARBY software, and its subversions, are being used since many years by the
INFN Milano-Bicocca group for different applications, like the efficiency
evaluation in gamma spectroscopy with HPGE diodes, or the investigation of the
residual contamination sources and the development of accurate background
models in rare event experimental searches with bolometers (see e.g.
cuore0-model ; cuore-budget ).

In the case of JUNO, a configuration file was built to reconstruct with ARBY
the main detector components: the LS, the acrylic sphere, the SS truss, the
large and small PMT systems together with their readout electronics, the
calibration equipment, the water pool, and the veto system. The different
contamination sources were then systematically positioned in each of these
materials and the resulting experimental spectrum evaluated with ARBY. Some
details of the reconstructed geometries are shown in Figure 12.

##  Appendix C G4-LA simulation code

The G4-LA simulation code is based on GEANT4 toolkit using the
G4RadioactiveDecay class in order to generate the decays of all the
radionuclides of interest. The relevant contaminants (mostly U and Th chains
and 40K) are generated uniformly in the bulk materials. The subsequent
particles are tracked and the deposited energy as well as the coordinates of
the mean deposited energy in the LS are recorded, allowing energy and FV cuts.
The quenching effect for alpha and electrons particles has been taken into
account as described in the paper. The detector geometry is simplified and
includes the most critical materials of JUNO (LS, acrylic vessel and inner
water pool). This allows a precise crosscheck of the energy spectra and the
derived singles rate for LS and acrylic vessel in order to validate the Monte
Carlo electromagnetic simulation of SNiPER.

## Acknowledgements

We are grateful for the ongoing cooperation from the China General Nuclear
Power Group. This work was supported by the Chinese Academy of Sciences, the
National Key R&D Program of China, the CAS Center for Excellence in Particle
Physics, Wuyi University, and the Tsung-Dao Lee Institute of Shanghai Jiao
Tong University in China, the Institut National de Physique Nucléaire et de
Physique de Particules (IN2P3) in France, the Istituto Nazionale di Fisica
Nucleare (INFN) in Italy, the Italian-Chinese collaborative research program
MAECI-NSFC, the Fond de la Recherche Scientifique (F.R.S-FNRS) and FWO under
the “Excellence of Science – EOS” in Belgium, the Conselho Nacional de
Desenvolvimento Científico e Tecnològico in Brazil, the Agencia Nacional de
Investigacion y Desarrollo in Chile, the Charles University Research Centre
and the Ministry of Education, Youth, and Sports in Czech Republic, the
Deutsche Forschungsgemeinschaft (DFG), the Helmholtz Association, and the
Cluster of Excellence PRISMA+ in Germany, the Joint Institute of Nuclear
Research (JINR) and Lomonosov Moscow State University in Russia, the joint
Russian Science Foundation (RSF) and National Natural Science Foundation of
China (NSFC) research program, the MOST and MOE in Taiwan, the Chulalongkorn
University and Suranaree University of Technology in Thailand, and the
University of California at Irvine in USA.

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