# Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Angel Abusleme  Thomas Adam  Shakeel Ahmad  Rizwan Ahmed  Sebastiano Aiello
Muhammad Akram  Abid Aleem  Fengpeng An  Qi An  Giuseppe Andronico  Nikolay
Anfimov  Vito Antonelli  Tatiana Antoshkina  Burin Asavapibhop  João Pedro
Athayde Marcondes de André  Didier Auguste  Weidong Bai  Nikita Balashov
Wander Baldini  Andrea Barresi  Davide Basilico  Eric Baussan  Marco Bellato
Marco Beretta  Antonio Bergnoli  Daniel Bick  Lukas Bieger  Svetlana
Biktemerova  Thilo Birkenfeld  Iwan Morton-Blake  David Blum  Simon Blyth
Anastasia Bolshakova  Mathieu Bongrand  Clément Bordereau  Dominique Breton
Augusto Brigatti  Riccardo Brugnera  Riccardo Bruno  Antonio Budano  Jose
Busto  Anatael Cabrera  Barbara Caccianiga  Hao Cai  Xiao Cai  Yanke Cai
Zhiyan Cai  Stéphane Callier  Antonio Cammi  Agustin Campeny  Chuanya Cao
Guofu Cao  Jun Cao  Rossella Caruso  Cédric Cerna  Vanessa Cerrone  Chi Chan
Jinfan Chang  Yun Chang  Auttakit Chatrabhuti  Chao Chen  Guoming Chen
Pingping Chen  Shaomin Chen  Yixue Chen  Yu Chen  Zhangming Chen  Zhiyuan Chen
Zikang Chen  Jie Cheng  Yaping Cheng  Yu Chin Cheng  Alexander Chepurnov
Alexey Chetverikov  Davide Chiesa  Pietro Chimenti  Yen-Ting Chin  Ziliang Chu
Artem Chukanov  Gérard Claverie  Catia Clementi  Barbara Clerbaux  Marta
Colomer Molla  Selma Conforti Di Lorenzo  Alberto Coppi  Daniele Corti  Simon
Csakli  Flavio Dal Corso  Olivia Dalager  Jaydeep Datta  Christophe De La
Taille  Zhi Deng  Ziyan Deng  Xiaoyu Ding  Xuefeng Ding  Yayun Ding  Bayu
Dirgantara  Carsten Dittrich  Sergey Dmitrievsky  Tadeas Dohnal  Dmitry
Dolzhikov  Georgy Donchenko  Jianmeng Dong  Evgeny Doroshkevich  Wei Dou
Marcos Dracos  Frédéric Druillole  Ran Du  Shuxian Du  Katherine Dugas
Stefano Dusini  Hongyue Duyang  Jessica Eck  Timo Enqvist  Andrea Fabbri
Ulrike Fahrendholz  Lei Fan  Jian Fang  Wenxing Fang  Marco Fargetta  Dmitry
Fedoseev  Zhengyong Fei  Li-Cheng Feng  Qichun Feng  Federico Ferraro  Amélie
Fournier  Haonan Gan  Feng Gao  Alberto Garfagnini  Arsenii Gavrikov  Marco
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Gornushkin  Alexandre Göttel  Marco Grassi  Maxim Gromov  Vasily Gromov
Minghao Gu  Xiaofei Gu  Yu Gu  Mengyun Guan  Yuduo Guan  Nunzio Guardone  Cong
Guo  Wanlei Guo  Xinheng Guo  Caren Hagner  Ran Han  Yang Han  Miao He  Wei He
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Shaojing Hou  Yee Hsiung  Bei-Zhen Hu  Hang Hu  Jianrun Hu  Jun Hu  Shouyang
Hu  Tao Hu  Yuxiang Hu  Zhuojun Hu  Guihong Huang  Hanxiong Huang  Jinhao
Huang  Junting Huang  Kaixuan Huang  Wenhao Huang  Xin Huang  Xingtao Huang
Yongbo Huang  Jiaqi Hui  Lei Huo  Wenju Huo  Cédric Huss  Safeer Hussain
Leonard Imbert  Ara Ioannisian  Roberto Isocrate  Arshak Jafar  Beatrice
Jelmini  Ignacio Jeria  Xiaolu Ji  Huihui Jia  Junji Jia  Siyu Jian  Cailian
Jiang  Di Jiang  Wei Jiang  Xiaoshan Jiang  Xiaoping Jing  Cécile Jollet
Philipp Kampmann  Li Kang  Rebin Karaparambil  Narine Kazarian  Ali Khan
Amina Khatun  Khanchai Khosonthongkee  Denis Korablev  Konstantin Kouzakov
Alexey Krasnoperov  Sergey Kuleshov  Nikolay Kutovskiy  Loïc Labit  Tobias
Lachenmaier  Cecilia Landini  Sébastien Leblanc  Victor Lebrin  Frederic
Lefevre  Ruiting Lei  Rupert Leitner  Jason Leung  Demin Li  Fei Li  Fule Li
Gaosong Li  Huiling Li***Now at SHANDONG INSTITUTE OF ADVANCED TECHNOLOGY,
Shandong, China Jiajun Li  Mengzhao Li  Min Li  Nan Li  Qingjiang Li  Ruhui Li
Rui Li  Shanfeng Li  Tao Li  Teng Li  Weidong Li  Weiguo Li  Xiaomei Li
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Xiaohui Qi  Sen Qian  Xiaohui Qian  Zhen Qian  Hao Qiao  Zhonghua Qin
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Alessandra Re  Abdel Rebii  Mariia Redchuk  Bin Ren  Jie Ren  Barbara Ricci
Komkrit Rientong  Mariam Rifai  Mathieu Roche  Narongkiat Rodphai  Aldo Romani
Bedřich Roskovec  Xichao Ruan  Arseniy Rybnikov  Andrey Sadovsky  Paolo
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Michaela Schever  Cédric Schwab  Konstantin Schweizer  Alexandr Selyunin
Andrea Serafini  Mariangela Settimo  Vladislav Sharov  Arina Shaydurova
Jingyan Shi  Yanan Shi  Vitaly Shutov  Andrey Sidorenkov  Fedor Šimkovic
Apeksha Singhal  Chiara Sirignano  Jaruchit Siripak  Monica Sisti  Mikhail
Smirnov  Oleg Smirnov  Thiago Sogo-Bezerra  Sergey Sokolov  Julanan
Songwadhana  Boonrucksar Soonthornthum  Albert Sotnikov  Ondřej Šrámek
Warintorn Sreethawong  Achim Stahl  Luca Stanco  Konstantin Stankevich  Hans
Steiger  Jochen Steinmann  Tobias Sterr  Matthias Raphael Stock  Virginia
Strati  Alexander Studenikin  Aoqi Su  Jun Su  Shifeng Sun  Xilei Sun  Yongjie
Sun  Yongzhao Sun  Zhengyang Sun  Narumon Suwonjandee  Michal Szelezniak
Akira Takenaka  Jian Tang  Qiang Tang  Quan Tang  Xiao Tang  Vidhya Thara
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Ushakov  Vadim Vedin  Carlo Venettacci  Giuseppe Verde  Maxim Vialkov  Benoit
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Voronin  Lucia Votano  Pablo Walker  Caishen Wang  Chung-Hsiang Wang  En Wang
Guoli Wang  Jian Wang  Jun Wang  Li Wang  Lu Wang  Meng Wang  Meng Wang
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Yangfu Wang  Yaoguang Wang  Yi Wang  Yi Wang  Yifang Wang  Yuanqing Wang  Yuyi
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Wei  Wenlu Wei  Yadong Wei  Yuehuan Wei  Kaile Wen  Liangjian Wen  Jun Weng
Christopher Wiebusch  Rosmarie Wirth  Bjoern Wonsak  Diru Wu  Qun Wu  Yiyang
Wu  Zhi Wu  Michael Wurm  Jacques Wurtz  Christian Wysotzki  Yufei Xi  Dongmei
Xia  Fei Xiao  Xiang Xiao  Xiaochuan Xie  Yuguang Xie  Zhangquan Xie  Zhao Xin
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Taylor Yan  Xiongbo Yan  Yupeng Yan  Changgen Yang  Chengfeng Yang  Jie Yang
Lei Yang  Xiaoyu Yang  Yifan Yang  Yifan Yang  Haifeng Yao  Jiaxuan Ye  Mei Ye
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Guojun Yu  Hongzhao Yu  Miao Yu  Xianghui Yu  Zeyuan Yu  Zezhong Yu  Cenxi
Yuan  Chengzhuo Yuan  Ying Yuan  Zhenxiong Yuan  Baobiao Yue  Noman Zafar
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Honghao Zhang  Jialiang Zhang  Jiawen Zhang  Jie Zhang  Jingbo Zhang  Jinnan
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Zhang  Shu Zhang  Shuihan Zhang  Siyuan Zhang  Tao Zhang  Xiaomei Zhang  Xin
Zhang  Xuantong Zhang  Yinhong Zhang  Yiyu Zhang  Yongpeng Zhang  Yu Zhang
Yuanyuan Zhang  Yumei Zhang  Zhenyu Zhang  Zhijian Zhang  Jie Zhao  Rong Zhao
Runze Zhao  Shujun Zhao  Dongqin Zheng  Hua Zheng  Yangheng Zheng  Weirong
Zhong  Jing Zhou  Li Zhou  Nan Zhou  Shun Zhou  Tong Zhou  Xiang Zhou  Jingsen
Zhu  Kangfu Zhu  Kejun Zhu  Zhihang Zhu  Bo Zhuang  Honglin Zhuang  Liang Zong
Jiaheng Zou  Jan Züfle

###### Abstract

Core-collapse supernova (CCSN) is one of the most energetic astrophysical
events in the Universe. The early and prompt detection of neutrinos before
(pre-SN) and during the SN burst is a unique opportunity to realize the multi-
messenger observation of the CCSN events. In this work, we describe the
monitoring concept and present the sensitivity of the system to the pre-SN and
SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is
a 20 kton liquid scintillator detector under construction in South China. The
real-time monitoring system is designed with both the prompt monitors on the
electronic board and online monitors at the data acquisition stage, in order
to ensure both the alert speed and alert coverage of progenitor stars. By
assuming a false alert rate of 1 per year, this monitoring system can be
sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc
and SN neutrinos up to about 370 (360) kpc for a progenitor mass of
30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing
ability of the CCSN is evaluated by using the accumulated event anisotropy of
the inverse beta decay interactions from pre-SN or SN neutrinos, which, along
with the early alert, can play important roles for the followup multi-
messenger observations of the next Galactic or nearby extragalactic CCSN.

##  1 Introduction

Core-collapse supernovas (CCSN) are one of the most energetic astrophysical
events, accompanying the death of a massive star. A burst of neutrinos of tens
of MeV energies plays important roles during its explosion and carries away
most of the released gravitational binding energy of around $10^{53}$ erg.
This overall picture is essentially supported by the detection of sparse
neutrinos from SN 1987A in the Large Magellanic Cloud [1, 2, 3]. For the next
Galactic or nearby extra-galactic CCSN, more detailed time and energy spectra
information of neutrinos from the CCSN are highly desired to describe and
model the complex physical processes of the explosion. Such more detailed
picture will be achieved by different types of modern neutrino detectors with
lower energy threshold, larger target masses and complementary designs.
Moreover, the first detection of neutrinos emitted prior to the core collapse
(pre-SN) is also possible for nearby progenitor stars in current neutrino
detectors like KamLAND [4] and Super-Kamiokande [5] as well as the future ones
like Jiangmen Underground Neutrino Observatory (JUNO) [7, 6], which would
provide valuable information on the final stage of the stellar evolution and
related physics probes [8, 9].

The JUNO experiment, under construction in South China [7, 6], is designed
with a 20 kton liquid scintillator (LS) detector to primarily determine the
neutrino mass ordering using reactor neutrinos. It can also register both the
pre-SN and SN neutrinos via inverse beta decay (IBD), a dominant interaction
channel. Meanwhile, JUNO is also capable of detecting a SN burst using other
channels, such as elastic scattering processes on the electrons and free
protons, as well as the charged-current and neutral current interactions on
the carbon nuclei [7]. The detection of pre-SN and SN neutrinos offers us
enormous possibilities of studying physics of the CCSN explosion mechanism and
related neutrino properties. However, to fully characterize the grand picture
of the CCSN event, the observations of not only neutrinos but also other
messengers, i.e., gravitational waves and electromagnetic radiation, are
indispensable. Gravitational waves are produced following the violent
aspherical core collapse [10], which together with neutrinos are currently the
only direct real-time probes to the inner dynamics of the core collapse. The
early electromagnetic radiation leaks out after the shock waves propagate to
the envelope of the progenitor star, with a time delay of minutes to days
after the SN neutrinos from the neutrino-sphere [11, 12]. Therefore, all these
considerations motivate a real-time monitoring system in JUNO to provide early
alerts for follow-up multi-messenger observations of the next CCSN. In
addition to independent experimental efforts searching for the next CCSN, a
multi-experiment collaboration, SNEWS [13, 14], was created to further enhance
these capabilities.

In this work, we present the concept of the CCSN monitoring system at JUNO
detecting both the pre-SN neutrinos and SN neutrinos. The system is designed
with both prompt monitors on the electronic boards and online monitors at the
data acquisition (DAQ) stage. These prompt monitors aim to provide much
quicker alerts than the conventional online process to catch any potential
multi-messenger observations. Since a Galactic or near Extragalactic CCSN is
extremely rare, a specific trigger-less data processing scheme is also
developed to limit the loss of CCSN-related information. We evaluate the
sensitivity of the monitoring system to the CCSN, and its pointing ability
using the anisotropy of the online IBD events. Such precise directional
information would be important to help the optical telescopes to catch early
electromagnetic radiation. Also, the precise onset time of signal can be
useful for triangulating the source when combining information from several
experiments.

The remainder of this work is organized as follows. We introduce in Sec. 2 the
numerical models of the Pre-SN neutrinos and SN neutrinos from progenitor
stars. The JUNO detector and detection of the pre-SN and SN neutrinos are
presented in Sec. 3. The concept of the real-time monitoring of the CCSN is
depicted in Sec. 4. And then the sensitivity and pointing ability of the
monitoring system to the CCSN are evaluated in Sec. 5 and Sec. 6, respectively
with the simulated event samples for both pre-SN and SN neutrinos. Finally, we
conclude in Sec. 7.

##  2 Pre-Supernova and Supernova Neutrino Emission

The neutrino emission from massive stars heavier than 8 to 10 solar masses
($M_{\odot}$) commences from the fusion of hydrogen and becomes the dominant
source of stellar cooling over the photon radiation following the ignition of
carbon [15]. Starting from the phase of carbon burning, neutrinos are
dominantly produced in pairs through the thermal processes, i.e., the plasmon
decay $\gamma^{*}\to\nu+\overline{\nu}$, the photo-neutrino process
$\gamma+e^{-}\to e^{-}+\nu+\overline{\nu}$, the pair annihilation process
$e^{+}+e^{-}\to\nu+\overline{\nu}$ and the bremsstrahlung process $e^{-}+Ze\to
e^{-}+Ze+\nu+\overline{\nu}$, where $Ze$ denotes the heavy nuclei with an
atomic number of $Z$. There are also significant contributions from nuclear
weak interactions after the silicon burning [16, 17], including the $e^{\pm}$
capture and $\beta^{\pm}$ decays of the heavy nuclei. Prior to the core
collapse, these neutrinos $\nu_{e}$, $\overline{\nu}_{e}$, $\nu_{x}$ and
$\bar{\nu}_{x}$ ($\nu_{x}$ collectively stands for $\nu_{\mu}$, $\nu_{\tau}$
and $\bar{\nu}_{x}$ for their antiparticles) with energies of O(1) MeV are
usually called the pre-SN neutrinos.

At the end of the stellar evolution of massive stars, an iron core is formed
and grows by the silicon shell burning. This core collapses inward under
gravity once the Chandrasekhar mass limit is reached, which results in the
subsequent CCSN [18]. A neutrino burst (called SN neutrinos) of $\sim$10 s
with average energy of tens of MeV and higher luminosity than pre-SN neutrinos
are expected to be produced in three main phases [19, 20, 21], namely, the
shock breakout burst phase, the post-bounce accretion phase and the proto-
neutron star cooling phase. The SN neutrinos are neither obscured by the
interstellar dust as the electromagnetic radiations, nor totally absent for
failed explosions resulting in a black hole, highlighting the importance of
neutrino detection for the observation of the CCSN.

In this work, we employ different numerical models for the fluxes of pre-SN
neutrinos and SN neutrinos to study the influence of different models. The
pre-SN models are from Patton et al. [17] for the $15\ M_{\odot}$ and $30\
M_{\odot}$ progenitor stars, where both thermal processes and nuclear weak
interactions are taken into account in the pre-SN simulation. The SN neutrino
models are provided by the Nakazato group [21] and the Garching group [22].
The Nakazato models are simulated for progenitor masses of $13\ M_{\odot}$ and
$30\ M_{\odot}$ with metallicities and shock revival times of (0.004, 100ms)
and (0.002, 300ms) respectively. The Garching models are simulated up to the
cooling phase with the equation of state LS220, including the convection via a
mixing-length scheme and the effect of nucleon potentials on the neutrino
opacity for the progenitor masses of $11.2\ M_{\odot}$ and $27\ M_{\odot}$.
Note that the time duration of Nakazato models lasts for about 20 s while that
of Garching models cuts off at about 3 to 4 s. All the pre-SN and SN models
provide the time-dependent luminosity and energy spectra for different flavor
neutrinos. The neutrino fluxes at the detector can be obtained with the scale
factor of $1/D^{2}$ with $D$ being the distance to the progenitor star of
CCSN. It should be noted that the definitions for the zero point of time are
different in the pre-SN and SN models, where it refers to the core collapse
time for pre-SN models while it is defined as the shock bounce time for SN
models.

The pre-SN neutrinos and SN burst neutrinos undergo a flavor conversion when
propagating from the core to the terrestrial detectors. In the pre-SN phase,
collective oscillations [23, 24] can be neglected due to the low number
density of neutrinos in the medium, but that is not the case for SN neutrinos,
due to the large quantity of neutrinos produced during the explosion. From the
neutrino-sphere to around one thousand kilometers, the neutrino-neutrino
refraction may lead to the energy spectral split of SN neutrinos [23].
However, it is still unclear whether the collective neutrino oscillations do
happen in a real supernova environment [24]. For simplicity, we temporarily
neglect the collective neutrino oscillations, but one should keep in mind that
they may have important effects on the detection of SN neutrinos. Even farther
out from the neutrino-sphere, the Mikheyev-Smirnov-Wolfenstein (MSW) matter
effects [25, 26] will play an important role and leave an imprint on the
neutrino spectra [27]. According to the current neutrino oscillation data
[28], the resonant flavor conversions for both pre-SN and SN neutrinos are
highly adiabatic and depend on the neutrino mass ordering. Therefore the
neutrinos fluxes $F_{\nu}$ at the Earth can be expressed in terms of the
initial ones $F^{0}_{\nu}$ at production in the following forms [27]:

$\displaystyle F_{\nu_{e}}$ $\displaystyle=$ $\displaystyle
pF^{0}_{\nu_{e}}+(1-p)F^{0}_{\nu_{x}}\;,$ (2.1) $\displaystyle
F_{\overline{\nu}_{e}}$ $\displaystyle=$
$\displaystyle\bar{p}F^{0}_{\overline{\nu}_{e}}+(1-\bar{p})F^{0}_{\overline{\nu}_{x}}\;,$
(2.2) $\displaystyle F_{\nu_{x}}$ $\displaystyle=$ $\displaystyle
0.5(1-p)F^{0}_{\nu_{e}}+0.5(1+p)F^{0}_{\nu_{x}}\;,$ (2.3) $\displaystyle
F_{\overline{\nu}_{x}}$ $\displaystyle=$ $\displaystyle
0.5(1-\bar{p})F^{0}_{\overline{\nu}_{e}}+0.5(1+\bar{p})F^{0}_{\overline{\nu}_{x}}\;,$
(2.4)

where $p=\sin^{2}\theta_{13}\approx 0.022$ and
$\overline{p}=\cos^{2}\theta_{13}\cos^{2}\theta_{12}\approx 0.687$ for the
normal neutrino mass ordering (NO), and
$p=\sin^{2}\theta_{12}\cos^{2}\theta_{13}\approx 0.291$ and
$\overline{p}=\sin^{2}\theta_{13}\approx 0.022$ for the inverted neutrino mass
ordering (IO). The upper panels of Fig. 1 show the examples of neutrinos
fluxes for pre-SN at 0.2 kpc and SN at 10 kpc respectively. Finally, Earth
matter effects are not included for either the pre-SN neutrinos or SN
neutrinos.

##  3 JUNO Detector and Neutrino Signals

JUNO is a multi-purpose neutrino experiment under construction at Jiangmen in
Guangdong Province, China. The complete detector of JUNO consists of the
central detector (CD), the water pool (WP) and the top tracker (TT) [29], all
are placed about 700 m underground for shielding from cosmic rays.

The CD is composed of 20 kt LS contained in a spherical acrylic vessel with a
35.4 m inner diameter. The LS is composed of linear alkylbenzene as the
solvent, 2,5-diphenyloxazole (PPO) as the fluor and
p-bis-(o-methylstyryl)-benzene (bis-MSB) as the wavelength shifter [30]. It is
the main target for the pre-SN and SN neutrinos at JUNO. To detect the light
induced by the final-state particles of neutrino interactions, 17,612 20-inch
PMTs (LPMTs) and 25,600 3-inch PMTs are installed on the outer stainless steel
latticed shell of CD with 40.1 m diameter. JUNO can reach an excellent energy
resolution of about 3%@1 MeV [31] and its vertex resolution can be 10 cm at 1
MeV [32]. The veto system with the WP and TT is designed to tag the muons with
high efficiency and suppress the cosmogenic backgrounds, where the WP contains
35 kton ultrapure water and serves also as a passive shielding for the
radioactivity from surrounding rocks. Its muon detection efficiency is greater
than 99% with about 2,400 20-inch PMTs installed on the outer surface of the
stainless steel Shell Structure of the CD. The TT reuses the plastic
scintillating strips from the OPERA experiment [33] and is placed on top of
the WP detector to track muons.

JUNO can achieve a low energy threshold for the purpose of the pre-SN and SN
neutrino detection. Currently the default global trigger strategy of CD
applies a typical multiplicity trigger algorithm. It can efficiently suppress
the backgrounds mostly from the dark noise and intrinsic radioactivity of
${}^{14}{\rm C}$ and can reach an energy threshold of ${\rm O}(0.1)$ MeV [34].

---
Figure 1: The neutrino fluxes (upper panels) and the expected visible energy
spectra (bottom panels) for pre-SN neutrinos of 30 $M_{\odot}$ Patton model at
0.2 kpc integrated over the last 5 days before the core collapse (left panels)
and for SN neutrinos of 30 $M_{\odot}$ Nakazato model at 10 kpc (right
panels), estimated with 20 kton LS for both the NO and IO cases. The IBD
prompt energy from the annihilated positron is related to the neutrino energy
as $E_{\rm vis}\approx E_{\nu}-0.8$ MeV. For the $e$ES channel, $E_{\rm vis}$
is estimated with the kinetic energy of the recoiled electron. For the $p$ES
channel, $E_{\rm vis}$ refers to the light emitted from the quenched recoiled
proton. The corresponding detector response between $E_{\rm vis}$ and
$E_{\nu}$ can be found in [39]

The energies of SN neutrinos are several tens of MeV, so in JUNO, all flavors
of SN neutrinos can be registered via multiple channels. The dominant channel,
which exists exclusively for $\bar{\nu}_{e}$, is the IBD process
$\overline{\nu}_{e}+p\rightarrow e^{+}+n$, with a coincidence signature of the
prompt signal from the positron’s kinetic energy deposition and annihilation
and the delayed signal from 2.2 MeV $\gamma$ from the neutron capture on
hydrogen. The subdominant channels are neutrino-proton elastic scattering
($p$ES), $\nu+p\rightarrow\nu+p$, and neutrino-electron elastic scattering
($e$ES), $\nu+e^{-}\rightarrow\nu+e^{-}$, with single visible signals from
energy deposition of the proton or electron. In addition, the charged-current
(CC) and neutral-current (NC) interactions of neutrinos on ${}^{12}{\rm C}$
nuclei are also accessible for SN neutrinos of the higher energy range. The
energies of pre-SN neutrinos are in the order of O(1) MeV. So the interactions
of neutrinos on the carbon nuclei are not accessible for pre-SN neutrinos.
Meanwhile, the visible energy of $p$ES events are below threshold due to the
severe proton quenching effect. Thus only the IBD and $e$ES interactions are
available for pre-SN neutrinos.

The neutrino fluxes of different pre-SN and SN models with the MSW effect
considered are convoluted with the energy-dependent interaction cross sections
for the IBD [35], $p$ES [36], $e$ES [37] and the neutrino interactions on the
carbon nuclei [38]. The final-state particles of the pre-SN and SN
interactions are generated in the time sequence and propagated to the JUNO
detector simulation. In Fig. 1 we illustrate the expected visible energy
spectra in the JUNO LS detector for the IBD and $e$ES interaction channels of
the pre-SN neutrinos using the $30M_{\odot}$ Patton model integrated over the
last 5 days before core collapse at 0.2 kpc (left panel), and for the dominant
IBD, $e$ES and $p$ES interaction channels of the SN neutrinos using the
$30M_{\odot}$ Nakazato model at 10 kpc (right panel) for both NO and IO cases.
With a 0.2 MeV energy threshold assumed, the time evolution of the expected
IBD events for pre-SN neutrinos (left panel) and SN neutrinos (right panel)
are illustrated in Fig. 2. The event rate increases gradually within several
days before collapse and undergoes a sudden increase within a few seconds as
core collapse. This characteristic can be used to distinguish whether a CCSN
happens.

The IBD is a golden channel for both pre-SN neutrinos and SN burst neutrinos,
thanks to a large number of free protons in the LS medium and largest cross
section at MeV energies. The IBD events can be tagged with a high efficiency
because of the coincident prompt and delayed signals. For the pre-SN
neutrinos, the number of IBD events depends crucially on the neutrino mass
ordering, which is suppressed in the IO case by a factor of three or four
compared with that in the NO case. The $p$ES and $e$ES elastic scattering
channels are sensitive to all flavor neutrinos with smaller cross sections
than that of the IBD channel. For the SN neutrinos, they dominate in the lower
energy range below the IBD threshold, which implies that the energy threshold
of the LS detector plays an important role for the extraction of $p$ES and
$e$ES events. Although the event separation of $p$ES and $e$ES events are more
difficult than the IBD channel because they are single signals, it still can
be realized by using the pulse shape discrimination method [40]. For the pre-
SN neutrinos, most of the $e$ES events appear in the energy range below 2 MeV,
which are highly contaminated with the backgrounds from radioactivity and
cosmogenic isotopes. In contrast to IBD events, the numbers of $e$ES events
are much higher in the IO case than that in the NO case, which provides an
opportunity to probe the mass ordering with pre-SN neutrinos [9]. In this
paper, IBD events are mainly considered in the monitoring systems, while other
channels will be considered in the future.

---
Figure 2: The IBD time evolution of pre-SN neutrinos with Patton models for a
progenitor at 0.2 kpc before collapse (left) and SN neutrinos with Nakazato
models for a CCSN at 10 kpc (right). Here, 0.2 MeV energy threshold is
assumed. The same definition of IBD events is applied as in Fig. 1.

##  4 Concept of the CCSN Monitoring System

The real-time monitoring system of the CCSN in JUNO aims to provide early
alerts and record the CCSN-related data as much as possible for the next
Galactic or near extragalactic CCSN. It features a redundant design consisting
of the prompt monitor embedded in both the global trigger and Multi-messenger
trigger electronic boards, and the online monitor at the DAQ stage. The prompt
monitor aims to reduce the timing latency of issuing an alert for a CCSN
event, while the online monitor operates with the finely reconstructed
information at the DAQ stage and can extract the fundamental CCSN
characteristics in a rapid and effective way.

The pre-SN neutrinos and SN neutrinos will produce an increase of the event
rate compared to the rate of the continuous stable sources, e.g., reactor
neutrinos, solar neutrinos, radioactivity and cosmic muons. By monitoring the
rate of events with characteristics similar to those expected from a CCSN,
this real-time monitoring system will release an alert once a change of the
steady event rate is identified. More details will be discussed in Sec. 5.
Then an early alert will be sent to the internal collaboration and
astronomical communities for follow-up multi-messenger observations of the
CCSN. In the remaining parts of this section, more detailed description of the
system will be introduced, including the PMTs readout and trigger electronics,
the prompt monitor and online monitor systems.

###  4.1 Large PMT readout electronics

As discused in [6] the analog signal coming from the JUNO 20-inch PMTs is fed
to a readout electronics board, the so-called Global Control Unit (GCU) which
amplifies the PMT analog signal and converts it to a digital waveform with a
14 bit ADC and a sampling rate of 1 GS/s. Each GCU handles three 20-inch PMTs,
in parallel, and it is installed on the CD support structure, very close to
the PMTs, inside a water-tight box. The digitized PMT waveform is further
processed in a local FPGA and trigger primitives are generated. All local
trigger information are send to the electronics rooms where they are validated
by the Central Trgger Unit (CUT) and, in parallel elaborated by the Multi-
messenger trigger boards. The CTU validates the trigger signal (based on
simple multiplicity requirements or more complicated event topology) and issue
the trigger validation for the waveform to be read-out. In parallel to the
single PMT waveform read-out stream, the waveform integrated charge is
computed on each GCU FPGAs and sent with the event timestamp, in asynchronous
mode, to the DAQ for each PMT signal. The latter stream allows a higher data
rate due to the reduced size of the transmitted information. Finally, in order
to handle exceptional high rate events, like those coming from a nearby
Galactic CCSN, a dedicated 2 GBytes DDR memory has been placed on each GCU
readout board to provide a local storage of the events in case of a nearby
Galactic CCSN. According to evaluation and readout tests, the readout system
will be able to handle and record all the events for a CCSN explosion at 0.5
kpc or further.

###  4.2 Prompt Monitor on Trigger Boards

The prompt monitoring system runs on FPGAs of the electronic boards in the
global trigger system, which is aimed to reduce the time latency of issuing an
alert for the followup multi-messenger observations of the next CCSN. It can
continuously monitor core-collapse supernovae by detecting an increase in
event rate of supernova neutrinos.

The information of fired LPMTs collected from GCUs is processed on the global-
trigger board with a 16 ns clock cycle. An event is identified based on the
PMT multiplicity condition or a more refined topological distribution of the
fired LPMTs [34]. The acquisition is anticipated to have an energy threshold
of $O(0.1)$ MeV, where the backgrounds from the radioactivity of ${}^{14}{\rm
C}$ and coincidence of LPMT dark noises are mostly filtered. In normal
operation, the global-triggered event rate is approximately 1 kHz in CD.

Once there is a global-triggered event in CD, the total number of LPMT hits of
this event $N_{\rm hit}$ is counted within a time window of $T_{\rm hit}$
(e.g., 1 $\mu$s) opened right after the global-triggered timestamp. Obviously,
$N_{\rm hit}$ is generally proportional to the visible energy of the particle
deposited in LS, which can be used to discriminate SN neutrinos with energies
of $O(10)$ MeV from other kinds of neutrino sources and the radioactivity
signals. Furthermore, in order to suppress background induced by cosmic muons,
the trigger information of WP is introduced to the global-trigger board in
real time. The WP trigger is activated by a multiplicity algorithm with a
threshold value larger than the number of fired LPMTs caused by SN neutrinos
in WP. The global-triggered CD events are vetoed for a time period of 1.5 ms
after the WP trigger timestamp. With SN neutrinos as the diagnostic tool of
the CCSN, monitoring candidates are selected outside the veto period with
$N_{\rm low}<N_{\rm hit}<N_{\rm high}$, where the current $N_{\rm high}$ and
$N_{\rm low}$ are chosen to map onto the total numbers of hits corresponding
to the visible energies of around 10 MeV and 40 MeV respectively. In such a
scenario, the calibration sources [31] are also excluded from the SN neutrino
candidates. When a critical increase in the event rate is detected in the time
series of SN neutrino candidates, it will trigger a prompt alert of the
candidate CCSN event. Immediately the onsite computer, the DAQ manager and
calibration system will be informed, and then the DAQ servers will store the
corresponding data that will be described in Sec. 4.3.

---
Figure 3: (Left panel) The number of LPMT hits $N_{\rm hit}$ versus the
visible energy of positrons. The red line is the average number of LPMT hits
with respect to the visible energy. (Right panel) The $N_{\rm hit}$
distributions from different triggered sources: the prompt signals of reactor
neutrinos, muons going through CD, cosmogenic isotopes and SN neutrinos using
Nakazato model with $30~{}M_{\odot}$. The dashed lines indicate the selection
criteria of prompt monitor candidates corresponding to false alert rate being
1/month. Note that the triggered rate of SN neutrinos has been averaged over
the CCSN duration of 10 s in the right panel.

The relationship between $N_{\rm hit}$ and visible energy $E_{\rm vis}$ in LS
is evaluated with the simulated positron samples, as is shown in the left plot
of Fig. 3. The two dashed lines correspond to $N_{\rm low}$ and $N_{\rm
high}$, which are used to select SN neutrino candidates. Tab. 1 summarizes the
expected numbers of the signal candidates induced by SN neutrinos using
different numerical models, which dominantly come from the prompt signals of
IBD interactions (about 80%) and partly from the $e$ES and neutrino
interactions on ${}^{12}{\rm C}$ (about 20%). Also, the right plot of Fig. 3
illustrates the distribution of $N_{\rm hit}$ from different sources,
including all signals of SN neutrinos, the prompt signal of reactor neutrinos,
muons going through CD and long life-time cosmogenic isotopes from Ref. [45].
Here, the event rates are scaled by different factors for comparison and that
for SN is averaged over the CCSN duration of 10 s. There are many other
signals which have either small energy or negligible event numbers in the time
duration of $O(10)$ s, so that their contributions to the background can be
neglected. Also, it is clear from this plot that if we do not apply muon veto
using WP trigger, muons will significantly increase the background rate.
Hence, a 1.5 ms muon veto is introduced. The muon rate at the JUNO detector is
about 3.9 Hz with an average energy of 207 GeV [7]. The WP trigger designed
for the prompt monitor can tag the muons passing through the WP detector with
the track length greater than 1 m with an efficiency larger than 99.5%. The
un-tagged muons are mainly due to the corner clipper muons with a short track
length and the muons only passing through the surrounding rocks. Fast neutrons
produced by these un-tagged muons in rocks may reach the LS detector and
induce global-triggered events. They are evaluated with detailed simulations
in the JUNO framework and can introduce a background rate of less than 13 per
day in the prompt monitor. For the tagged muons, the background candidates
after a 1.5 ms veto are mainly from long life-time isotopes of the cosmic muon
spallation process. In order to satisfy the false alert rate (see Sec. 5)
requirements of the prompt monitoring system, two different values of $N_{\rm
low}$ are chosen to suppress background. Hence, referring to the isotope
yields in Ref. [45], the expected background rate is estimated to be about
83/day or 45/day according to the $N_{\rm low}$ cuts, with contributions from
the 12B, 8B, 8Li, 9C, 9Li, 11Be nuclei and the fast neutron.

###  4.3 Online Monitor at the DAQ Stage

In parallel to the prompt monitoring system on the electronic boards, another
online monitor system will be developed at the DAQ stage to maximize the alert
coverage of the progenitor stars. The online monitor is implemented at the
software level by utilizing the finely reconstructed events. It will operate
in full time and support two online monitors for either the pre-SN neutrinos
or SN neutrinos to diagnose the candidate CCSN event. A specific data flow and
the triggerless data-saving strategy are included to maximize recording of
CCSN-related data. Below, we can see that the online monitor performs event
reconstruction and selects IBD events, which can greatly reduce backgrounds,
especially for low-energy pre-SN neutrinos. Hence, the online monitor can also
utilize pre-SN neutrinos for early warning, while the prompt monitor can not
due to the lack of reconstructed information.

Let us first introduce the design of the data processing flow. The data
streams from all sub-detectors are collected by the DAQ servers. The
triggerless data of LPMTs in CD are grouped into events based on the online
software trigger, and further packed with data from other sub-detectors for
online event reconstruction. The software trigger algorithm for the
triggerless data of CD can achieve an energy threshold of $O(0.1)$ MeV, which
is similar to that of the global trigger strategy, but it can be further
optimized in the future since all the raw LPMT hit information is available.
To reduce the data size, the online fast filtering algorithm is first used and
then the delicate reconstruction to get the event information, such as the
energy and vertex of a CD event or the muon track in the veto system. These
reconstructed events are assembled in time sequence and used in the online
Pre-SN Monitor and SN Monitor.

As discussed in Sec. 3, the pre-SN and SN neutrinos are detectable in LS via
several distinct neutrino interaction channels. A sudden increase of event
rates from these channels can help to diagnose a CCSN candidate. In this work,
we concentrate on the online reconstructed information of IBD events to
evaluate the baseline performance for both the Pre-SN Monitor and SN Monitor.
Further optimization can be obtained with more interaction channels. The IBD
event candidates for the Pre-SN Monitor (denoted as "$pre$IBD") are selected
in CD with an IBD selection criteria:

  * •

Event vertex must be in the fiducial volume with $r<17.2$ m,

  * •

The prompt signal is required to be within the energy window of 0.7 MeV
$<E_{\rm p}<$3.4 MeV and the delayed signal with 1.9 MeV $<E_{\rm d}<$ 2.5
MeV.

  * •

The coincidence of prompt and delayed signals are further filtered with their
time interval $\Delta T_{\rm d-p}<$1.0 ms and the vertex distance $R_{\rm
d-p}<1.5$ m.

And the same muon veto criteria as in Ref. [44] are used:

  * •

For muons tagged by the WP detector, veto the whole LS volume for 1.5 ms.

  * •

For muons with well reconstructed tracks in CD, veto those candidate events
with reconstructed vertices less than 1 m, 2 m, and 4 m away from the
corresponding muon tracks for 0.6 s, 0.4 s and 0.1 s, respectively. For muons
without properly reconstructed tracks, veto the whole LS volume for 0.5 s.

  * •

A 1.2 s veto is applied for all candidate events reconstructed inside a sphere
with the radius of 3 m around the reconstructed vertex for spallation neutron
captures.

The IBD event candidates for the SN Monitor (denoted as "$sn$IBD") are
selected with similar IBD criteria as $pre$IBD except for a different prompt
energy cut of $E_{\rm p}>E_{\rm low}$, where $E_{\rm low}$ uses different
values to satisfy the false alert rate requirement (5.5 MeV and 7.5 MeV for
1/month and 1/year) like the prompt monitor case. The muon veto criteria for
$pre$IBD is not feasible to $sn$IBD any more due to the short burst time of
$O(10)$ s. The muon veto criteria for the $sn$IBD selection is only to veto
the whole LS volume for 1.5 ms if muons are tagged by the WP detector. During
the calibration period, these candidates will be further selected outside of
the default calibration regions, which is feasible given the effective
communication between the calibration and DAQ systems.

Figure 4: The expected energy spectra of IBD events from SN neutrinos with the
Nakazato model at 10 kpc, pre-SN neutrinos integrated in the last day before
core collapse with the Patton model at 0.2 kpc and the sum of continuous IBD
backgrounds, which mainly come from reactor neutrinos at a distance of 53 km,
geo-neutrinos, $\beta$-n decays of cosmogenic ${}^{9}{\rm Li}/^{8}{\rm He}$,
fast neutrons, accidental coincidence and ${}^{13}{\rm C}(\alpha,n)^{16}{\rm
O}$ reaction [44].

In the LS detector of JUNO, the IBD background can be contributed by reactor
neutrinos emitted from reactor cores of 26.6 ${\rm GW_{th}}$ thermal power at
a distance of about 53 km, geo-neutrinos, $\beta$-n decays of the cosmogenic
${}^{9}{\rm Li}/^{8}{\rm He}$, fast neutrons, accidental coincidence events
and the radiogenic process of ${}^{13}{\rm C}(\alpha,n)^{16}{\rm O}$ reaction
[44]. Fig. 4 shows the energy spectra of IBDs from SN neutrinos, pre-SN
neutrinos and the backgrounds. The energy range of backgrounds and IBDs of
pre-SN neutrinos are the same. So when selecting $pre$IBD candidates, the
upper boundary of the prompt energy cut $E_{p}<3.4$ MeV is optimized by
maxizing the signal-to-background ratio using pre-SN neutrinos of the 15
$M_{\odot}$ Patton model in the last day before core collapse. The residual
backgrounds of the $pre$IBD candidates are about 21/day in total, as
summarized in Tab. 1. It is contributed by reactor neutrinos, 9Li/8He, geo-
neutrinos and others, with the yield being 18.2, 0.2, 1.2 and 1.4 per day
respectively. And in terms of the $sn$IBD candidates, the total background
rate is estimated to be about 39/day (13/day) for $E_{\rm low}=5.5$ MeV
($E_{\rm low}=7.5$ MeV), as summarized in Tab. 1. The breakdown of this
background rate for each source is 8.3/day (0.4/day) from reactor neutrinos,
30.6/day (12.9/day) from 9Li/8He and 0.3/day (0.1/day) from other sources. The
difference of background contribution between $pre$IBD and $sn$IBD stems from
the different energy range and muon veto strategy.

Once the $pre$IBD and $sn$IBD candidates are selected, the Event Accumulator
will keep all of them for the online CCSN characterization, together with
other possible events according to the specific requirements. Once a critical
value of the candidate event rate is reached in the Pre-SN monitor or SN
Monitor, an alert will be sent to the Alert Processer. At the Alert Processor,
there are three defined types of alert status. The first type is a SN alert,
which is triggered when the SN monitor detects an alert. The second type is a
pre-SN alert, which is triggered when the Pre-SN monitor detects an alert. The
third type is a nearby alert, which is triggered when both the SN monitor and
Pre-SN monitor detect alerts within a certain time frame, such as 10 days.

The Fast Characterization is designed to provide in time characterization of
the CCSN candidate, e.g., the CCSN direction, energy spectra and light curves
with the input events from the Event Accumulator. Here we take the CCSN
direction reconstruction (cf. Sec. 6) as illustration to present the basic
procedures. In case of an SN alert or nearby alert, a time window of 20 s is
opened from 1 s before to 19 s after the alert timestamp. All the $sn$IBD
events within this time window are combined to reconstruct the CCSN direction.
In particular for a nearby alert, which indicates that a nearby Galactic CCSN
may have occurred and a large amount of SN candidate events would flood into
the DAQ system, the challenges to the DAQ computing ability should also be
carefully treated. Therefore it is possible that the Event Reconstruction and
its subsequent processes are stopped by DAQ to handle such a specific case,
and the online monitor will not be able to reconstruct CCSN direction based on
SN neutrino signals itself. Fortunately, even in this extreme case there is
still an opportunity to extract the direction of the nearby CCSN if a certain
number of $pre$IBD events are available. The reconstructed direction will be
updated as more $pre$IBD are accumulated. All the reconstructed direction
information will be sent to the onsite computer and further aid the telescopes
to constrain the sky coverage. In the end, the Alert Processer will inform the
DAQ manager in case of an SN alert or nearby alert in order to store the raw
triggerless T/Q data within a predefined time interval (e.g. $\pm$60 s). The
storage of the normal data streams is not affected at the same time. Such a
design can limit the loss of unforeseen information of the CCSN and preserve
extra potential in the future.

##  5 Sensitivity of the CCSN Early Alerts

In the real-time monitoring system, an intrinsic problem of detecting a CCSN
candidate is to identify the transient variation of the event rates induced by
the pre-SN neutrinos or SN neutrinos. The background rates of the candidate
signals are relatively stable and could be precisely determined and updated by
in-situ measurements. To search for an event rate increase compared to the
background, we apply a sliding event method where the time interval $\Delta T$
of the $N$ latest sequential candidates is calculated. If the $\Delta T$ is
smaller than a predefined time interval threshold $\Delta T_{\rm thr}$, a CCSN
candidate is identified and the alert will be released. This sliding event
method is equivalent to the typical sliding window method, which counts the
number of events within a fixed time window, but the sliding event method
provides a finer granularity to tune the alert system when the number of
events N is small.

The choice of $\Delta T_{\rm thr}$ and $N$ in the sliding event method depends
on the time duration of pre-SN neutrinos or SN neutrinos and the alert
performance of the monitoring system, which are generally quantified using
four parameters:

  * •

The alert efficiency of correctly releasing a CCSN alert with pre-SN neutrinos
or SN neutrinos. It can be estimated using Monte Carlo samples, calculating
the fraction of samples that give alerts.

  * •

The alert distance at where the alert efficiency reaches 50%.

  * •

The alert time that the monitoring system releases an CCSN alert. In the
following, the alert time is evaluated to be the time relative to the collapse
time for pre-SN neutrinos, and the bounce time for SN neutrinos. In reality,
the time latency due to the data transfer, the candidate selection and
criteria tests is estimated to be around $O(1)$ s for the prompt monitor and
$O(1)$ minute for the online monitor.

  * •

The false alert rate (FAR) induced by the background statistical fluctuation.
Note that choosing parameters that increases the SN coverage and reduces the
alert time, would simultaneously increase the FAR.

In the following part of this section, Monte Carlo simulations of pre-SN
neutrinos and SN neutrinos are performed with the JUNO official framework [6]
to evaluate the capability of the monitoring system for the CCSN. The
simulation includes a full chain of the event generator, detector simulation,
electronics simulation, trigger algorithm and event reconstruction. The time
series of background candidates are produced separately in a toy Monte Carlo
manner and mixed with the CCSN-related events during the monitoring procedure.
The FAR under different sets of $\Delta T_{\rm thr}$ and $N$ are quantified
using random background candidates with the proper Poisson fluctuation for the
online monitor. However, note that in the prompt monitor case the background
mainly comes from the cosmogenic isotopes, whose multiplicity might be larger
than one in a single muon. Therefore, both the multiple production of the
isotopes in a single muon and the random coincidence of these single events
from different muons should be considered when one estimate the false alert
rate of the CCSN in the selected time window. Unexpected transient signals
(i.e. possible PMT flashers) that may cause false alert will be considered in
the future. In the following, the settings will be chosen so the conservative
FAR baseline of 1/month and 1/year are adopted for the JUNO monitoring system.
As already mentioned before, the prompt monitor will not be used for the pre-
SN monitor because of the much lower event rate and neutrino energies.
However, since the online monitor employs the reconstructed information and
event selection, it is applicable for both pre-SN and SN monitors.

###  5.1 Sensitivity of the Prompt Monitor

Following the logic of the prompt monitor on the global-trigger board as
discussed in Section 4, the sensitivity of the prompt monitor to SN neutrinos
is evaluated with the global trigger system. As discussed in Section 4.2,
different values of $N_{\rm low}$ are used to meet the requirement of having a
FAR smaller than 1/month or 1/year. The corresponding background rates are
estimated to be 83/day and 45/day, respectively.

---
Figure 5: Expected alert efficiency (left panel) and alert time relative to
the bounce moment (right panel) for the prompt monitor with SN neutrinos of
Nakazato and Garching models at different distances. FAR of 1/month (top row)
and 1/year (bottom row) are assumed.

The performance of the prompt monitor is evaluated with simulated SN neutrino
events from both the Nakazato and Garching models. In the sliding event method
of the prompt monitor, the time interval threshold $\Delta T_{\rm thr}$ is
determined with a fixed number of event candidates $N=3$ and is set to 7.5 s
and 2.1 s for a FAR of 1/month and 1/year, respectively. The results on the
alert efficiency and alert time for different models are shown in Fig. 5,
while the alert distance and alert time at $10$ kpc of the prompt monitor is
summarized in the right part of Tab. 1. The alert time after the core bounce
at each distance is defined as the 68% upper bound of the issued alert time
for all tested samples. As shown in Tab. 1 and the right panel of Fig. 5, the
intrinsic alert time after bounce is about $20\sim 30$ ms for the Nakazato
models and $10\sim 20$ ms for the Garching models for a CCSN with the distance
of 10 kpc. At this distance, the alert efficiency is 100% and the alert time
is determined by the event rate at early stage. So usually, CCSN progenitors
with larger mass and IO will have higher event rate at early stage (e.g. the
Nakazato models shown in the right plot of Fig. 2) and hence be alerted
earlier.

| Model |  Mass [$M_{\odot}$] |  Mass ordering |  $r_{bkg}$ [$day^{-1}$] | $N_{IBD}$ | $N_{sel}$ | Alert distance [kpc] | Alert time
---|---|---|---|---|---|---|---|---
FAR<1/month | FAR<1/year | FAR<1/month | FAR<1/year
SN | Garching | 11 | NO |  39 (83) | 1675 | 1414 | 230 | 230 | (16 ms) | (17 ms)
(1204) | (220) | (190)
IO | 1676 | 1413 | 230 | 230 | (13 ms) | (14 ms)
(1228) | (220) | (200)
27 | NO | 3132 | 2651 | 320 | 320 | (15 ms) | (16 ms)
(2466) | (310) | (280)
IO | 2958 | 2502 | 310 | 310 | (13 ms) | (13 ms)
(2366) | (300) | (270)
Nakazato | 13 | NO | 2326 | 1934 | 270 | 240 | (20 ms) | (21 ms)
(1698) | (240) | (200)
IO | 2827 | 2365 | 300 | 270 | (16 ms) | (17 ms)
(2190) | (280) | (240)
30 | NO | 5074 | 4098 | 400 | 370 | (31 ms) | (31 ms)
(4217) | (390) | (350)
IO | 4972 | 4131 | 390 | 350 | (31 ms) | (31 ms)
(4145) | (370) | (330)
pre-SN | Patton | 15 | NO | 21 | 659 | 556 | 1.3 | 1.1 | -140 h | -120 h
IO | 196 | 156 | 0.7 | 0.6 | -90 h | -30 h
30 | NO | 1176 | 930 | 1.7 | 1.6 | -220 h | -180 h
IO | 379 | 302 | 1.0 | 0.9 | -100 h | -3 h
Table 1: Summary of the background rate ($r_{bkg}$), expected number of
signals ($N_{IBD}$) and selected candidates ($N_{sel}$), alert distance and
alert time for different models. The values outside the bracket intend for
online monitor while that inside the bracket intend for prompt monitor. When
selecting the signals, $N_{\rm hit}$ is used for the prompt monitor and IBD
events with different energy ranges are used for online SN and pre-SN monitor,
refer to text for details. And the $N_{IBD}$ is estimated with different
models @10kpc for the SN neutrinos and @0.2kpc within the last day before core
collapse for the pre-SN neutrinos. $r_{bkg}$ and $N_{sel}$ for prompt monitor
and online SN monitor is estimated using the selection criteria corresponding
to FAR being 1/month.

###  5.2 Sensitivity of the Online Monitor

The online monitor has potential sensitivities to both pre-SN neutrinos and SN
neutrinos through finely reconstructed and filtered candidates, which could
provide early alerts for a nearby Galactic progenitor star before core
collapse as well as a Galactic or nearby extragalatic CCSN during explosion.

####  5.2.1 Pre-SN neutrinos

As mentioned in Section 4.3, the online monitor can utilize either the pre-SN
neutrinos or SN neutrinos for the early warning purpose. The detection of pre-
SN neutrinos can provide an early alert for an imminent CCSN, which can be
realized with the monitoring of the $pre$IBD candidates as described in Sec.
4.3. Sec. 4.3 has already discussed the selection of $pre$IBD candidates and
its background, whose event rate is estimated to be about 21/day. The
performance of the online pre-SN monitor is evaluated with the sliding event
method, whose parameters are determined in the case of a FAR of 1/month or
1/year. The performance of alert efficiency to nearby progenitor stars is
shown in Fig. 6 using the Patton models. The corresponding alert distances and
alert time at $0.2$ kpc are summarized in the right part of Tab. 1. In the
worst scenario of IO and the 15 $M_{\odot}$ Patton model, the online pre-SN
monitor with a FAR of 1/year would be sensitive to the progenitor stars within
0.6 kpc away from the Earth, where about 22 nearby massive stars are covered
according to the list of candidate pre-SN stars of Ref. [46].

---
Figure 6: Alert efficiency (left panel) and alert time (right panel) of the
online Pre-SN Monitor at different distances for progenitor stars of $\rm
15M_{\odot}$ and $\rm 30M_{\odot}$ Patton models for the NO and IO cases. The
FAR is 1/month (upper panel) and 1/year (lower panel).

Betelgeuse is often taken as a representative of the nearby massive progenitor
stars and a candidate source of pre-SN neutrinos. To compare with pre-SN
studies in KamLAND [4] and Super-Kamiokande with Gadolinium [5], we assume two
extreme conditions of 0.15 kpc and 15 $M_{\odot}$, and 0.25 kpc and 30
$M_{\odot}$ for Betelgeuse, instead of the up-to-date estimation of the
distance and mass as in Ref. [47]. The expected alert time of a Betelgeuse-
like star in the online pre-SN monitor turns out to be 141 hr (84 hr) and 73
hr (2.6 hr) before the core collapse for the $15M_{\odot}$ and $30M_{\odot}$
Patton models with NO (IO) assuming the false alert rate is 1/year. Even in
the worst case of IO and a FAR of 1/year, a Betelgeuse-like star at 0.25 kpc
with 30 $M_{\odot}$ will still cause an alert about 2.6 hr before the core
collapse.

####  5.2.2 SN neutrinos

As discussed in Sec. 4.3, different values of $E_{low}$ are used to meet the
requirement of having a FAR smaller than 1/month and 1/year. The corresponding
background rates are estimated to be 39/day and 13/day, respectively. The
performance is also evaluated with the sliding event method, whose parameters
are determined in the case of a FAR of 1/month or 1/year. However, for the
online SN monitor, we shall focus on the performance in terms of the alert
efficiency rather than the alert time. Because due to the large amount of
events accumulated over a short time, the time latency of a SN alert in the
online monitor is dominated by the DAQ processing speed, which is anticipated
to be at the minute level and will be evaluated with future in situ
measurements. The alert efficiency is shown in Fig. 7 for the Nakazato and
Garching models. A set of parameters $N$ and $T_{\rm thr}$ are chosen as 3 and
15.8 s for $\rm FAR<1/month$ (left panel) and 3 and 4.5 s for $\rm FAR<1/year$
(right panel) in the sliding event method. The alert distance is also
summarized in the right part of Tab. 1. For the Garching models, the alert
efficiency are the same when constraining the FAR from 1/month to 1/year,
which is due to the limited time duration (about 3 to 4 s) of the available
data of these models. From the figure, one can observe that the online monitor
can be sensitive to the CCSN within 230 kpc even in the worst case of the
current adopted models, which covers the Milky Way and 51 nearby galaxies
according to the catalog of satellite galaxies in Ref. [48].

---
Figure 7: Alert efficiency of the online SN Monitor for the CCSN at different
distances using 13$M_{\odot}$ and 30$M_{\odot}$ Nakazato models for both the
NO and IO cases. The FAR in the left plot is 1 per month while is 1 per year
in the right plot.

##  6 CCSN pointing

The CCSN directionality is crucial to guide the telescopes to catch the early
light of the CCSN by focusing on the targeted sky area. JUNO will not only
contribute to the early alert of the CCSN, which can be used for triangulation
pointing along with other worldwide neutrino experiments [49], but also
provide an individual CCSN direction by using the collective anisotropy of the
accumulated IBD events from pre-SN neutrinos or SN neutrinos at the DAQ stage.

As studied in Refs. [50, 51], IBD events in the LS maintain a moderate
direction information of the incoming neutrinos. The neutrons of the IBD
events are emitted in a slightly forward angle relative to the neutrino
direction and undergo nearly isotropic scattering and diffusion processes
before being captured on hydrogen. The positrons of the IBD events are assumed
to annihilate with electrons at their production vertex. This is a reasonable
assumption considering that the average displacement of $O(0.1)$ cm is much
smaller than the positron reconstructed vertex resolution [32]. Therefore the
direction of the CCSN could be reconstructed using the collective anisotropy
of the IBD events from SN or pre-SN neutrinos:

$\displaystyle\vec{d}=\frac{1}{N}\sum_{i=1}^{N}{\vec{X}^{i}_{\rm np}}\;,$
(6.1)

where $N$ is the total number of IBD events from pre-SN neutrinos or SN
neutrinos, $\vec{X}^{i}_{\rm np}$ (for $i=1,2,\cdots,N$) denote the unit
vector from the reconstructed neutron vertex to the reconstructed positron
vertex for each IBD event. Obviously, the accuracy of the reconstructed CCSN
direction is statistics-dependent. In the following evaluation of the pointing
ability using full-chain simulated events of the pre-SN neutrinos or SN
neutrinos, we use the half-aperture $\theta_{0.68}$ of the cone around the
true CCSN direction, which contains 68$\%$ of all sorted results, as the
uncertainty of CCSN pointing. The direction reconstruction will be part of the
online fast characterization of the CCSN following the logic discussed in Sec.
5.

Figure 8: The number of $pre$IBD (left panel) or $sn$IBD (right panel)
candidates and the respective CCSN pointing ability as functions of the
progenitor distance. In both figures, the number of IBDs is on the left axis
and the half-aperture is on the right axis. Note that in the left panel, 0.2
kpc is the distance to Betelgeuse. Also, the pointing ability depends on the
number of IBDs, which is scaled to the distance of progenitor stars using 15
$M_{\odot}$, IO Patton model for $pre$IBDs (number of IBD candidates is
integrated over the last day before core collapse) and 13 $M_{\odot}$, IO
Nakazato model for $sn$IBDs.

###  6.1 Pre-SN Pointing

Due to the relatively small statistics of $pre$IBD events within several days
for a nearby progenitor star, the contribution of the backgrounds in the
direction reconstruction should not be neglected, among which the IBD events
from reactor neutrinos play the dominant role. Within one day, the relative
direction angle $\theta_{\rm sn,reactor}$ between pre-SN neutrinos and reactor
neutrinos varies with time due to the rotation of the Earth. Therefore three
cases at $\theta_{\rm sn,reactor}=0^{\circ}$, $\theta_{\rm
sn,reactor}=90^{\circ}$ and $\theta_{\rm sn,reactor}=180^{\circ}$ are taken
into account, where the performance of $\theta_{\rm sn,reactor}=0^{\circ}$ is
expected to be the best one and the case of $\theta_{\rm
sn,reactor}=90^{\circ}$ somehow corresponds to a realistic average of the pre-
SN neutrinos changing direction during the day due to the rotation of Earth.
Given a fixed number of background candidates as 21 per day, the reconstructed
direction uncertainty varies with the number of $pre$IBD events in the three
cases as shown in the left plot of Fig. 8. As the distance increases, there
will be less $pre$IBD events and the performance of direction reconstruction
becomes worse. In practice, the pointing ability of the pre-SN should be
between those in the cases of $\theta_{\rm sn,reactor}=180^{\circ}$ and
$\theta_{\rm sn,reactor}=0^{\circ}$ and we use the $\theta_{\rm
sn,reactor}=90^{\circ}$ case as a reference. A more detailed reconstruction
that accounts for the real direction of pre-SN neutrinos during the
observation time would still need to be implemented in the future.

According to the collection of pre-SN progenitor candidates in Ref. [46],
there are 31 red and blue CCSN progenitors within 1 kpc, which is shown as the
blue points of Fig. 9 in the equatorial coordinate system (J2000). Taking the
well-known progenitor star Betelgeuse as an example, we assume that pre-SN
neutrinos are produced according to the $15M_{\odot}$ Patton model at 0.2 kpc.
Following the logic of the online pre-SN monitor, there are 556 (156) $pre$IBD
events in the last one day before core collapse in the NO (IO) case.
Therefore, the pre-SN pointing with an uncertainty of $56^{\circ}$
($81^{\circ}$), estimated from the moderate case of $\theta_{\rm
sn,reactor}=90^{\circ}$ in Fig. 8, is expected for the NO (IO) case, as
illustrated by the area surrounded with the red (yellow) line in Fig. 9. Such
direction information can aid the astronomers to constrain CCSN progenitors at
the very last stages of the stellar evolution.

---
Figure 9: This figure is a sky-map that shows the pointing resolution for pre-
SN and SN cases: The red and blue CCSN progenitors within 1 kpc in the
equatorial coordinate system are shown by black dots. The brown and yellow
curves circle the 68% regions of the reconstructed direction for Betelgeuse
pre-SN star at 0.2 kpc with NO and IO respectively. The shadowed areas
surrounded with the orange and green lines show the pointing precision for a
SN at 10 kpc with NO and IO respectively.

###  6.2 SN Pointing

The background contribution in the CCSN direction reconstruction with the
$sn$IBD events is negligible over a short time scale. In the right plot of
Fig. 8 we illustrate the number of the $sn$IBD events and the corresponding
pointing ability with the 13$M_{\odot}$ Nakazato model and IO at different
distances. Assuming a typical CCSN at the Galactic center (RA:
$17^{h}45^{m}40.04^{s}$, Dec: $-29^{\circ}00^{\prime}28.1^{\prime\prime}$) of
10 kpc, which is shown as the triangle marker in Fig. 9, the average number of
$sn$IBD events for SN direction reconstruction is about 2000 in the NO case
and 2400 in the IO case with SN neutrinos from the 13 $M_{\odot}$ Nakazato
Model. As a result, the reconstructed direction uncertainty is about
$26^{\circ}$ in the NO case and $23^{\circ}$ in the IO case as shown in the
shadowed areas surrounded with the orange and green lines of Fig. 9. Very
close (e.g. closer than 1 kpc) SN would require special simulation tools and
reconstruction methods to take into account the high event rates and possible
pile-up effects. This situation will not be discussed in this paper.

##  7 Conclusion

Neutrinos produced from a massive star before and during core collapse form a
burst of the pre-SN and SN neutrinos, respectively. The early and prompt
detection of the pre-SN and SN neutrinos provides a unique opportunity to
realize the multi-messenger observation of the CCSN events. In this work, we
describe the concept of the real-time monitoring system in JUNO and present
its sensitivity. The system consists of prompt monitors on the trigger boards
and online monitors at the DAQ stage to ensure both alert speed and alert
coverage of progenitor stars. A triggerless data processing scheme is also
developed to limit the loss of CCSN-related information.

JUNO is unique and has good capability for the CCSN neutrino observation
because of its large target mass, low background and multiple-flavor detection
channels. In this work, the bench marking background rates and the sensitivity
of the prompt monitor and online monitor have been quantified with full-chain
simulated event samples. Assuming a FAR of 1/year, the online pre-SN monitor
is sensitive to pre-SN stars up to 1.6 kpc (0.9 kpc) for a star of 30
$M_{\odot}$ in the NO (IO) case. For a Betelgeuse-like star at 0.15 kpc with
15 $M_{\odot}$ (0.25 kpc with 30 $M_{\odot}$), a pre-SN alert could be
released 141 (73) hr or 84 (2.6) hr before the core collapse in the NO (IO)
case. Both the prompt monitor and online monitor provide comparable
sensitivity to the SN neutrinos, where a CCSN up to 370 kpc (360 kpc) could be
covered for the NO (IO) case. The intrinsic alert latency is expected to be
short in the prompt monitor for SN neutrinos, which is about 20 ms for a 13
$M_{\odot}$ progenitor star with Nakazato model at 10 kpc. We have also
evaluated the CCSN pointing ability by using the accumulated IBD events from
pre-SN or SN neutrinos. For a Betelgeuse-like star, the pointing ability with
the pre-SN events is about $56^{\circ}$ ($81^{\circ}$) in the NO (IO) case for
the 15 $M_{\odot}$ Patton model. For a typical CCSN at 10 kpc, the pointing
ability with SN neutrinos is about $26^{\circ}$ ($23^{\circ}$) in the NO (IO)
case using the 13 $M_{\odot}$ Nakazato model.

In the future, better performance of the real-time system could be achieved by
developing more complex monitoring algorithms and by optimizing the candidate
selection with all possible interaction channels. In addition to the prompt
and online monitors based on the global trigger system, there will be an
independent Multi-messenger trigger system, with the goal of achieving an
ultra-low detection threshold for events of energies O(10) keV. The primary
goal of the Multi-messenger trigger system is to allow for the detection of
low-energy transient neutrino signals, and provide the CCSN monitor with an
extended energy band of all-flavor neutrinos to maximize the alert ability to
the global network of optical, gravitational and neutrino telescopes.
Therefore, these monitoring systems are effective in early alerts and the
information of the CCSN directionality would play an important role in the
multi-messenger observation of the next CCSN.

## 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 and ANID - Millennium Science Initiative Program -
ICN2019_044 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, University of California at
Irvine and the National Science Foundation in USA.

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