The Astrophysical Journal, 885:133 (14pp), 2019 November 10

https://doi.org/10.3847/1538-4357/ab4883© 2019. The American Astronomical Society. All rights reserved.

Sensitivity of Super-Kamiokande with Gadolinium to Low Energy Antineutrinos from
Pre-supernova Emission
C. Simpson1,2 , K. Abe2,3, C. Bronner3, Y. Hayato2,3, M. Ikeda3, H. Ito3, K. Iyogi3, J. Kameda2,3, Y. Kataoka3, Y. Kato3,
Y. Kishimoto2,3, Ll. Marti3, M. Miura2,3, S. Moriyama2,3, T. Mochizuki3, M. Nakahata2,3, Y. Nakajima2,3, S. Nakayama2,3,
T. Okada3, K. Okamoto3, A. Orii3, G. Pronost3, H. Sekiya2,3, M. Shiozawa2,3, Y. Sonoda3, A. Takeda2,3, A. Takenaka3, H. Tanaka3,
T. Yano3, R. Akutsu4, T. Kajita2,4, K. Okumura2,4, R. Wang4, J. Xia4, D. Bravo-Berguño5, L. Labarga5, P. Fernandez5,
F. d. M. Blaszczyk6, C. Kachulis6, E. Kearns2,6, J. L. Raaf6, J. L. Stone2,6, L. Wan6, T. Wester6, S. Sussman6, S. Berkman7, J. Bian8,N. J. Griskevich8, W. R. Kropp8, S. Locke8, S. Mine8, M. B. Smy2,8, H. W. Sobel2,8, V. Takhistov8,51, P. Weatherly8,
K. S. Ganezer9,52, J. Hill9, J. Y. Kim10, I. T. Lim10, R. G. Park10, B. Bodur11, K. Scholberg2,11, C. W. Walter2,11, A. Coffani12,
O. Drapier12, M. Gonin12, J. Imber12, Th. A. Mueller12, P. Paganini12, T. Ishizuka13, T. Nakamura14, J. S. Jang15, K. Choi16,
J. G. Learned16, S. Matsuno16, R. P. Litchﬁeld17, A. A. Sztuc17, Y. Uchida17, M. O. Wascko17, V. Berardi18, N. F. Calabria18,
M. G. Catanesi18, R. A. Intonti18, E. Radicioni18, G. De Rosa19, G. Collazuol20, F. Iacob20, L. Ludovici21, Y. Nishimura22, S. Cao23,
M. Friend23, T. Hasegawa23, T. Ishida23, T. Kobayashi23, T. Nakadaira23, K. Nakamura2,23, Y. Oyama23, K. Sakashita23,
T. Sekiguchi23, T. Tsukamoto23, KE. Abe24, M. Hasegawa24, Y. Isobe24, H. Miyabe24, Y. Nakano24, T. Shiozawa24, T. Sugimoto24,
A. T. Suzuki24, Y. Takeuchi2,24, A. Ali25, Y. Ashida25, T. Hayashino25, S. Hirota25, M. Jiang25, T. Kikawa25, M. Mori25,
KE. Nakamura25, T. Nakaya2,25, R. A. Wendell2,25, L. H. V. Anthony26, N. McCauley26, A. Pritchard26, K. M. Tsui26, Y. Fukuda27,
Y. Itow28,29, M. Murrase28, T. Niwa28, M. Taani28,53, M. Tsukada28, P. Mijakowski30, K. Frankiewicz30, C. K. Jung31, X. Li31,
J. L. Palomino31, G. Santucci31, C. Vilela31, M. J. Wilking31, C. Yanagisawa31,54, D. Fukuda32, M. Harada32, K. Hagiwara32,
T. Horai32, H. Ishino32, S. Ito32, Y. Koshio2,32, M. Sakuda32, Y. Takahira32, C. Xu32, Y. Kuno33, L. Cook1,2, D. Wark1,34,
F. Di Lodovico35, S. Molina Sedgwick35,55, B. Richards35,55, S. Zsoldos35,55, S. B. Kim36, R. Tacik37,38, M. Thiesse39,
L. Thompson39, H. Okazawa40, Y. Choi41, K. Nishijima42, M. Koshiba43, M. Yokoyama2,44, A. Goldsack1,2, K. Martens2,
M. Murdoch2, B. Quilain2, Y. Suzuki2, M. R. Vagins2,8, M. Kuze45, Y. Okajima45, M. Tanaka45, T. Yoshida45, M. Ishitsuka46,
R. Matsumoto46, K. Ohta46 , J. F. Martin47, C. M. Nantais47, H. A. Tanaka47, T. Towstego47, M. Hartz38, A. Konaka38,
P. de Perio38, S. Chen48, B. Jamieson49, J. Walker49, A. Minamino50, K. Okamoto50, and G. Pintaudi50
TheSuper-KamiokandeCollaboration2

1
Department of Physics, Oxford University, Oxford, OX1 3PU, UK; charles.simpson@physics.ox.ac.uk
Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo Institutes for Advanced Study, University of Tokyo, Kashiwa,
Chiba 277-8583, Japan
3
Kamioka Observatory, Institute for Cosmic Ray Research, University of Tokyo, Kamioka, Gifu 506-1205, Japan
4
Research Center for Cosmic Neutrinos, Institute for Cosmic Ray Research, University of Tokyo, Kashiwa, Chiba 277-8582, Japan
5
Department of Theoretical Physics, University Autonoma Madrid, E-28049 Madrid, Spain
6
Department of Physics, Boston University, Boston, MA 02215, USA
7
Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, V6T1Z4, Canada
8
Department of Physics and Astronomy, University of California, Irvine, Irvine, CA 92697-4575, USA
9
Department of Physics, California State University, Dominguez Hills, Carson, CA 90747, USA
10
Department of Physics, Chonnam National University, Kwangju 500-757, Republic of Korea
11
Department of Physics, Duke University, Durham NC 27708, USA
12
Ecole Polytechnique, IN2P3-CNRS, Laboratoire Leprince-Ringuet, F-91120 Palaiseau, France
13
Junior College, Fukuoka Institute of Technology, Fukuoka, Fukuoka 811-0295, Japan
14
Department of Physics, Gifu University, Gifu, Gifu 501-1193, Japan
15
GIST College, Gwangju Institute of Science and Technology, Gwangju 500-712, Republic of Korea
16
Department of Physics and Astronomy, University of Hawaii, Honolulu, HI 96822, USA
17
Department of Physics, Imperial College London, London, SW7 2AZ, UK
18
Dipartimento Interuniversitario di Fisica, INFN Sezione di Bari and Universitá e Politecnico di Bari, I-70125, Bari, Italy
19
Dipartimento di Fisica, INFN Sezione di Napoli and Universitá di Napoli, I-80126, Napoli, Italy
20
Dipartimento di Fisica, INFN Sezione di Padova and Universitá di Padova, I-35131, Padova, Italy
21
INFN Sezione di Roma and Universitá di Roma “La Sapienza,” I-00185, Roma, Italy
22
Department of Physics, Keio University, Yokohama, Kanagawa, 223-8522, Japan
23
High Energy Accelerator Research Organization (KEK), Tsukuba, Ibaraki 305-0801, Japan
24
Department of Physics, Kobe University, Kobe, Hyogo 657-8501, Japan
25
Department of Physics, Kyoto University, Kyoto, Kyoto 606-8502, Japan
26
Department of Physics, University of Liverpool, Liverpool, L69 7ZE, UK
27
Department of Physics, Miyagi University of Education, Sendai, Miyagi 980-0845, Japan
28
Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Aichi 464-8602, Japan
29
Kobayashi-Maskawa Institute for the Origin of Particles and the Universe, Nagoya University, Nagoya, Aichi 464-8602, Japan30
National Centre For Nuclear Research, 02-093 Warsaw, Poland
31
Department of Physics and Astronomy, State University of New York at Stony Brook, NY 11794-3800, USA
32
Department of Physics, Okayama University, Okayama, Okayama 700-8530, Japan
33
Department of Physics, Osaka University, Toyonaka, Osaka 560-0043, Japan
34
STFC, Rutherford Appleton Laboratory, Harwell Oxford, and Daresbury Laboratory, Warrington, OX11 0QX, UK
35
Department of Physics, King’s College London, London, WC2R 2LS, UK
36
Department of Physics, Seoul National University, Seoul 151-742, Republic of Korea
37
Department of Physics, University of Regina, 3737 Wascana Parkway, Regina, SK, S4SOA2, Canada

1The Astrophysical Journal, 885:133 (14pp), 2019 November 10

Simpson et al.

38

TRIUMF, 4004 Wesbrook Mall, Vancouver, BC, V6T2A3, Canada
Department of Physics and Astronomy, University of Sheffeld, S3 7RH, Sheffeld, UK
40
Department of Informatics in Social Welfare, Shizuoka University of Welfare, Yaizu, Shizuoka, 425-8611, Japan
41
Department of Physics, Sungkyunkwan University, Suwon 440-746, Republic of Korea
42
Department of Physics, Tokai University, Hiratsuka, Kanagawa 259-1292, Japan43
The University of Tokyo, Bunkyo, Tokyo 113-0033, Japan
44
Department of Physics, University of Tokyo, Bunkyo, Tokyo 113-0033, Japan
45
Department of Physics, Tokyo Institute of Technology, Meguro, Tokyo 152-8551, Japan46
Department of Physics, Faculty of Science and Technology, Tokyo University of Science, Noda, Chiba 278-8510, Japan47
Department of Physics, University of Toronto, ON, M5S 1A7, Canada
48
Department of Engineering Physics, Tsinghua University, Beijing, 100084, Peopleʼs Republic of China
49
Department of Physics, University of Winnipeg, MB R3J 3L8, Canada
50
Faculty of Engineering, Yokohama National University, Yokohama, Kanagawa, 240-8501, Japan
Received 2019 August 22; revised 2019 September 23; accepted 2019 September 25; published 2019 November 7
39

Abstract
Supernova detection is a major objective of the Super-Kamiokande (SK) experiment. In the next stage of SK (SKGd), gadolinium (Gd) sulfate will be added to the detector, which will improve the ability of the detector to identify
neutrons. A core-collapse supernova (CCSN) will be preceded by an increasing ﬂux of neutrinos and antineutrinos,from thermal and weak nuclear processes in the star, over a timescale of hours; some of which may be detected at
SK-Gd. This could provide an early warning of an imminent CCSN, hours earlier than the detection of theneutrinos from core collapse. Electron antineutrino detection will rely on inverse beta decay events below the usual
analysis energy threshold of SK, so Gd loading is vital to reduce backgrounds while maximizing detection
efﬁciency. Assuming normal neutrino mass ordering, more than 200 events could be detected in the ﬁnal 12hr
before core collapse for a 15–25 solar mass star at around 200pc, which is representative of the nearest red
supergiant to Earth, α-Ori (Betelgeuse). At a statistical false alarm rate of 1percentury, detection could be up to
10hr before core collapse, and a pre-supernova star could be detected by SK-Gd up to 600pc away. A presupernova alert could be provided to the astrophysics community following gadolinium loading.
Uniﬁed Astronomy Thesaurus concepts: Neutrino astronomy (1100); Supernova neutrinos (1666); Particle
astrophysics (96); Silicon burning (1457)
of cooling as their emission is highly temperature dependent.
For a nearby star, neutrino emission increases over a relatively
short timescale to detectable levels (Odrzywolek &Heger 2010), which could give advanced warning before corecollapse occurs. This earlier alert to the astronomy community
could aid in observing the early light from an SN. Advanced
warning could prevent SK (and other SN neutrino detectors)
from missing a nearby core collapse due to planned down-time.
SK has run for over 20yr so far, and has on average >90% uptime, making it especially useful for the detection of SNe,which are rare and could happen at any time. KamLAND,
another neutrino detection experiment, has a functioning preSN alert system (Asakura et al. 2016).
Furthermore, astrophysical neutrinos with known sources
have only ever been detected from the Sun (for a review, see
Kirsten 1999), from SN1987A, and blazar TXS 0506+056(The IceCube Collaboration et al. 2018). The only direct
observation of SN neutrinos so far is SN1987A, and neutrinocooled stars have never been observed, so detection would
contribute to our understanding of late stage stellar evolution(Odrzywolek et al. 2007; Kato et al. 2015; Yoshida et al. 2016;
Patton et al. 2017b).
In the next stage of the Super-Kamiokande experiment (SKGd), gadolinium sulfate will be added to the detector, which
will improve the ability of the detector to identify neutrons, andtherefore low energy ne through inverse beta decay (IBD).
Previous estimates (Odrzywolek et al. 2004; Patton et al.
2017b; Fernandez Menendez 2017) of SK’s ability to detect
pre-SN neutrinos have assumed the energy threshold of the SK
solar analysis (3.5 MeV positron kinetic energy) (Abe et al.
2016b). In fact, detection will be possible at lower energies,
albeit with reduced efﬁciency.

1. Introduction
A core-collapse supernova (CCSN) produces an ∼10s burstof neutrinos at tens of MeV, which is large enough that it canbe detected by Super-Kamiokande (SK) and other neutrino
detectors (Abe et al. 2016a) if it is in or near the Milky Way.
Much of what is known about galactic supernova explosions(SNe) comes from the detection of supernova (SN) neutrinos in1987 (Alekseev et al. 1988; Bratton et al. 1988; Hirata et al.
1988). In the case of another SN in or near our galaxy, thecurrent generation of neutrino detectors would be capable ofcollecting a much larger sample of SN neutrinos, improving
our understanding and resolving outstanding questions about
SNe. Neutrinos arrive before the electromagnetic radiation
produced by an SN, so can generate a warning enabling
astronomers to start observing as early as possible. Alertsystems already exist for this purpose, linking together manydetectors for maximum effect (Antonioli et al. 2004). SK has
the currently unique ability of determining the direction of an
SN from elastically scattered electrons, which is useful for
guiding optical instruments (Abe et al. 2016a); SN neutrino
detection is a major goal for SK.
Prior to collapse, as a star approaches the end of its life, thetemperature and density increases, causing neutrinos fromthermal and weak nuclear processes to become the main source
51

Also at Department of Physics and Astronomy, UCLA, CA 900951547, USA.
52
Deceased.
53
Also at School of Physics and Astronomy, University of Edinburgh,
Edinburgh, EH9 3FD, UK.
54
Also at BMCC/CUNY, Science Department, New York, New York, USA.
55
Currently at Queen Mary University of London, London, E1 4NS, UK.

2

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Simpson et al.

In this article, the sensitivity of the SK-Gd detector to thispre-SN neutrino ﬂux is assessed. The article is structured asfollows. Section 2 describes the pre-SN neutrino emission
processes, Section 3 gives necessary information concerning
SK-Gd, and theoretical pre-SN ﬂux estimates are discussed in
Section 4. The detection efﬁciency and backgrounds for low
energy IBD are assessed in Section 5. Results of the study arepresented in Section 6, followed by a conclusion in Section 7.

energies than those of an SN. Furthermore, pre-SN emission isover a very long timescale compared to SN emission. The mean
ne energy is below the IBD threshold, meaning only the tailcould possibly be detected through IBD; the increasing averageenergy will mean that the proportion above threshold will also
increase. For a sufﬁciently nearby star, SK-Gd would see a
rapid increase in the rate of low energy IBD candidate events.
This would presage a CCSN by hours.
From Nakamura et al. (2016; their Table 2), there are 41 redsupergiant (RSG) stars with distance estimates within 1kpc, 16
within 0.5kpc, and 5 within 0.2kpc. Famous nearby RSGs
include Betelgeuse α-Ori, Antares (α Sco), and ò Peg. Wolf–
Rayet stars are also possible supernova progenitors, e.g.,γ VelA.

2. Pre-supernova Neutrinos
In a massive star at the end of its life, the fusion ofhydrogen(H) and helium(He) nuclei is insufﬁcient to stabilize
the temperature and density of the star. That is, cooling fromradiation is greater than heating from fusion, leading tocontraction under gravity. The higher density, and hencetemperature, then enables the fusion of heavier nuclei, initiallyat the core of the star, and then in shells propagating outwards.
Neutrino emission is strongly temperature dependent. The
increased temperature leads to an increased rate of cooling due
to neutrino emission, which leads to further contraction andheating, and the fusion of even heavier nuclei (see Woosley
et al. 2002 for a review of late stellar evolution leading up to
SNe). This proceeds in stages driven by carbon(C), neon(Ne),oxygen(O), and silicon(Si) burning. Si burning creates an ironcore, which can lead to a CCSN; these stars are sometimes
called pre-supernova stars (pre-SN). Following the ignition of
C burning, neutrino emission is the greatest source of cooling(Odrzywolek et al. 2007); so these stars are also referred to as
neutrino-cooled stars.
The neutrino-cooled stage of a massive star’s life isremarkably short compared to usual astrophysical timescales,with the C burning stage lasting hundreds of years, Ne and O
stages lasting under a year, and Si burning lasting under two
weeks (Odrzywolek et al. 2004). While these processes occur
in the interior of the star, it may be that nothing changes in thestar’s electromagnetic emissions or at the outer surface of thestar, so such a state would not be observed by electromagnetic
astronomy (Odrzywolek et al. 2007).
Stars capable of CCSN are usually speciﬁed as having a zeroage main sequence (ZAMS) mass >8solarmasses(Me), i.e.,
those capable of CCSN. However, lighter stars in this category
might not enter the Si burning stage, instead undergoing corecollapse with an O–Ne–Mg core (Odrzywolek et al. 2007; Katoet al. 2015). Very massive stars >30Me may collapse directly
to a black holes as failed supernovae, but could still produce anincreasing and detectable neutrino ﬂux in late stages prior tocollapse (Patton et al. 2017a). Aside from ZAMS mass, stellar
evolution also depends on metallicity and rotation; the models
of pre-SN neutrino emission considered in this study assume
solar metallicity and no rotation effects.
Thermal processes, as well as β− decay and positron capture
contribute to ne emission. At these high temperatures a large
equilibrium population of positrons exists, leading to neutrino
emission by pair annihilation e+e-  nn , which is importantfor detection prospects due to the high ﬂux and relatively highaverage energy of the ne (Odrzywolek et al. 2007; Patton et al.
2017b).
SK-Gd has a chance of detecting a pre-SN star following theignition of Si burning, as the rate of ne emission (Figure 1(a)),
and crucially the ne average energy (Figure 1(b)), increase as
the star approaches core collapse (Odrzywolek & Heger 2010).
As shown by Figure 2, pre-SN ne emission is at much lower

3. Super-Kamiokande with Gadolinium
SK is a large water Cerenkov detector, and is well described
elsewhere (Fukuda et al. 2003; Abe et al. 2014). It consists
mainly of a tank of 50kilotons(kt) of ultra-pure water. The
inner detector (ID) is 32kt, and the ﬁducial volume (FV) is
usually given as 22.5kt, although in practice a smaller or larger
FV is used by different analyses as appropriate. In this paper
quoted efﬁciencies assume the full ID volume. The ID is
instrumented with around 11,000 50cm photomultiplier tubes(PMTs). Charged particles are detected through their emissionof Cerenkov radiation.
Super-Kamiokande with Gadolinium (SK-Gd, formerly
GADZOOKS!) is the next phase of the SK experiment. The
main aim of SK-Gd is to detect the supernova relic neutrinosignal within a few years of adding gadolinium (Gd; Beacom &Vagins 2004).
SK-Gd will add gadolinium sulfate (Gd2 (SO4)3) to SK’s purewater. Naturally abundant isotopes of Gd have some of thehighest thermal neutron capture (TNC) cross sections. TNC on
Gd is followed by a γ-ray cascade with a total energy of∼8MeV, much more than the single 2.2MeV γ-ray producedby TNC on hydrogen which is currently used at SK for neutron
tagging (Zhang et al. 2015). Mainly through Compton scatteredelectrons, γ-rayscan be detected in SK indirectly. The γ-ray
cascade from TNC on Gd produces visible energy comparable
to an electron with ∼4–5MeV total energy.
The main channel for detection of ne at low energy (roughly<10 MeV) in SK is IBD on hydrogen (H (ne, e+) n ), as its crosssection is relatively high. The neutron takes a short time tothermalize in water and capture, and travels only a shortdistance, meaning that the positron and TNC form a delayedcoincidence (DC), in which two events are reconstructed withina short time and distance of each other. This method of
detection is made possible by the upgrade to QBee electronics
described in Yamada et al. (2010) and Nishino et al. (2009).
The probability of uncorrelated events producing this signature
is low, so neutron tagging allows electron antineutrino events
to be distinguished from background events, including neutrino
events. The high TNC cross section of Gd makes the time
between the prompt and delayed parts of the event shorter thanwith H (∼20 μs versus ∼180 μs), and the higher visible energy
improves the vertex reconstruction resolution. As a result,tagging efﬁciency for signal will be higher, and accidental
backgrounds lower.
Note that in low energy IBD, the direction of the incoming necannot be reconstructed from the direction of the emitted
positron (Vogel & Beacom 1999), and the number of elastic
3

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Simpson et al.

Figure 1. Comparison of Lν number, and áEn ñ mean energy, of emitted ne against time to core collapse in models by Odrzywolek & Heger (2010) and Patton et al.
(2017b). Spikes in both quantities are caused by ignition of fusion of heavier isotopes in the core or shell of the star.

4. Electron Antineutrino Flux
Neutrino emissions are calculated from stellar models.
Although there are several sets of predictions published forthe ﬂux of ne from a pre-SN, this study primarily uses the data
sets of Odrzywolek & Heger (2010; data downloaded from
Odrzywolek, Web) and Patton et al. (2017b; data downloaded
from Patton et al. 2019). Patton et al. (2017b) predict similar ne
total emission rates to Odrzywolek & Heger (2010) for a 15 Me
star, as shown in Figures 1 and 3, though the time and energy
dependent emission rates differ. The ﬂux estimates of
Odrzywolek & Heger (2010) are calculated by post-processing
the output of an already existing stellar model, and isotopic
composition is calculated by assuming nuclear statisticalequilibrium (Odrzywolek 2009). Patton et al. (2017b, 2017a)
use a more modern stellar evolution code, which fully couples
the isotopic composition to the stellar evolution, and tracks therates of a larger number of isotopes individually. Isotopic
composition especially affects the rate of neutrinos produced
by weak nuclear processes.
Time t=0 is taken to be the moment at which the stellar
simulation is terminated (when the infall velocity exceeds somethreshold), which can be taken as the beginning of corecollapse. Figure 3 contrasts the time dependent predicted IBD
rates and the positron true energy spectra from the models
considered.
The electron ﬂavor ratio of the neutrinos is affected as it
passes through the dense stellar medium. Following Patton
et al. (2017b) and Kato et al. (2017), an adiabatic transition isassumed, and the ﬂavor is changed by the Mikheyev–Smirnov–
Wolfenstein high resonance, dependent on the neutrino massordering (MO). The assumed transition probability is
P (ne  ne ) = 0.675 (0.024) in the normal (inverted) massordering case, and P (nx  ne ) = 1 - P (ne  ne ). In the dataof Patton et al. (2017b), the nx ﬂux is provided. For the data of
Odrzywolek & Heger (2010), the initial nx ne ratio assumed tobe 0.19 following Asakura et al. (2016). This is assumed to not
be energy or time dependent, and includes the effect of vacuum
oscillations. Earth matter effects have not been included. Only
the electron antineutrino ﬂavor will interact through IBD, therest of the ﬂux is assumed to be invisible in this energy range
at SK.

Figure 2. Total emission vs. energy for ne from pre-SN and from an SN burst,
integrated over the full time range of the respective models. The dotted line
shows the reaction threshold of IBD. Pre-SN spectrum shown is a 30Memodel from Patton et al. (2017b), and SN spectrum is 30Me from Nakazato
et al. (2013). 30Me was chosen so that the two would have the same mass.
Note that the pre-SN neutrinos are emitted over a much longer timescale (1000hr vs. 20 s), and that the IBD cross section is strongly energy dependent.

scattering events will be small for a pre-SN, so this techniquewill not have any SN pointing ability. The direction of neutron
travel cannot be resolved in SK, so a positron-neutron vector
cannot be used to infer the ne direction either.
It is planned that SK’s ultra-pure water will be loaded withgadolinium sulfate in two steps, ﬁrst to 0.02% by mass, then to
0.2%; leading to 50% and 90% of neutrons capturing on Gd
respectively, with the rest mainly capturing on H. This paper
assumes 0.2% Gd loading, so it should be noted that SK-Gd
will begin with a period of reduced sensitivity.
Research and development of the required technologies for
SK-Gd has been undertaken by the EGADS experiment, which
has successfully operated a Gd-loaded water Cerenkov detector
for over two years at 0.2% gadolinium sulfate loading (Ikeda
et al. 2019).
4

The Astrophysical Journal, 885:133 (14pp), 2019 November 10

Simpson et al.

Figure 3. Comparison of the total number of IBDs in a 12hr window against time to core collapse, predicted in models by Odrzywolek & Heger (2010) and Patton
et al. (2017b). Detection efﬁciency is not taken into account. A distance of 200pc is assumed. Note that a reduced time range is used compared to Figure 1.
Interpolation has been used to produce equally spaced time and energy points.

From the ﬂux as a function of neutrino true energy and time,the expected rate of IBD reactions at SK-Gd are calculated. The
effect of distance is simply a factor of 1/R2. An accurate
approximation of the energy dependent cross section is used
(Strumia & Vissani 2003, Equation (25)). The number oftargets for IBD is the number of hydrogen nuclei in the SK ID.
Detection efﬁciency is dependent on the energy of the positron,and energy resolution is taken into account with a smearing
matrix calculated from detector simulation.
5. Detection Efﬁciency and Backgrounds
5.1. Energy Thresholds at SK-Gd
The most fundamental energy thresholds for SK-Gd areEν>1.8MeV for IBD, and Ee > 0.8MeV total energy for
Cerenkov emission by positrons. To a good approximation, thetotal energy Ee of a positron produced by IBD is related to theelectron antineutrino energy Eν by Ee=Eν−Δ, whereD = m n - mp = 1.293 MeV is the nucleon mass difference.
Positrons above the Cerenkov threshold may not be reconstructed if there are not enough photons detected. Cerenkov
photons detection inefﬁciencies arise due to attenuation in thewater, the photocathode coverage of the detector, and thequantum and collection efﬁciencies of the PMTs (Fukuda et al.
2003).
In SK, a PMT is considered a hit if the charge collected bythat PMT exceeds a threshold. In order to reduce the data ratefrom dark noise and radioactivity, a threshold is usually applied
to the number of hits in a time window (Abe et al. 2016b). The
Wide-band Intelligent Trigger (WIT) is an independent trigger
system at SK that uses parallel computing to reconstruct very
low energy events which do not meet the usual thresholds. It isclose to 100% efﬁcient for electrons around 4MeV totalenergy within the FV (Carminati 2015). Below this, theefﬁciency drops, as shown by Figure 4.
There is no way to reliably distinguish positrons at very low
energy (<4 MeV total) from the intrinsic radioactive background of SK, which increases by an order of magnitude foreach MeV the energy threshold is lowered. Requiring DC with

Figure 4. WIT efﬁciency against electron true total energy, evaluated withsimulation. Note that true energy is shown, not reconstructed energy. 5000 MC
events were generated for each 0.2MeV true energy bin. The efﬁciency forevents generated in the whole ID is compared to those generated only inside the
FV. The Cerenkov threshold and SK-IV solar neutrino analysis threshold(4 MeV total electron energy Abe et al. 2016b) are shown for reference.

a TNC on Gd can make background rates to IBD manageable,
even at the lowest energies SK can reconstruct. Furthermore,even if the IBD positron is not detected and successfullyreconstructed, the γ-ray cascade from TNC on Gd can bedetected. Such single neutron events will be subject to higher
backgrounds than those in DC, but the threshold in neutrinoenergy is effectively reduced to the threshold for the IBD
reaction (Eν>1.8 MeV).
5.2. Signal Event Model
Low energy positrons in 0.2MeV true total energy bins from
0.8 to 7MeV, as well as γ-ray cascades resulting from TNC on
Gd, were simulated using the standard detector simulation
Monte Carlo of SK (Koshio 1998).
The spectrum of γ-raysfrom TNC on Gd consists of a few
known energy γ-raysclose to the Q-value, and a continuum atlower energies where the levels are so densely populated that
5

The Astrophysical Journal, 885:133 (14pp), 2019 November 10

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Figure 5. Distribution of the reconstructed distance (a) and reconstructed time (b) between simulated event candidates (with 3 MeV prompt positrons) before
selection. Signal MC is shown in black and accidental background from data in red, area normalized to one.

thermalize, and τc the time for it to capture. Thermalization andcapture times were set to values measured using an americiumberyllium neutron source in EGADS loaded to 0.2% gadolinium sulfate by mass.

they are indistinguishable. How energy should be divided up
between the γ-raysin each event is not well known, so
approximate models are used. The SK detector simulation isbased on GEANT3, which does not simulate TNC on Gd by
default. GEANT4 (Agostinelli et al. 2003; Allison et al. 2016)
contains a number of generic models, but they do not containthose known high energy γ-rays. For the GLG4SIM genericliquid scintillator simulator, another model was developed(GLG4SIM 2006), which combined a parametric model of thecontinuum with the known high energy γ-rays. Recent efforts
at J-PARC (Ou et al. 2014; Das et al. 2017; Hagiwara et al.
2017) have used new measurements to account for what
combinations of γ-rayscome together, and have provided
another model including this as well as a different continuum
distribution. The two latter models give similar distributions of
the reconstructed variables, so it is likely that the detector isfairly insensitive to the details. The model used by GLG4SIM
was used as the basis for selection training and efﬁciency
calculations, with that provided by the J-PARC group used as across check. The difference between the two models is included
as an uncertainty.
Real data from the SK detector were used as backgroundnoise, into which were injected the PMT hits produced by thesimulated events. The hybrid data were then subjected to thesame triggering algorithms used by the WIT system. It was
assumed that all events had to be independently reconstructed
and selected by WIT. In the future, requirements onreconstruction quality could be loosened in the WIT for eventsin coincidence, or prompt and delayed vertices could be ﬁtsimultaneously, slightly improving triggering efﬁciencies forlow energy prompt events.
The important features of a DC are the temporal and spatial
separation of its two parts, shown in Figure 5. The distributionof spatial separation is dependent on the energy of the positronthrough the vertex reconstruction resolution, and is modeled bypairing positrons and neutrons created at the same true
locations in the detector simulation. Neutron transport was
not modeled, i.e., the neutrons were assumed to capture at thesame point at which they were produced. This is a reasonableassumption as, at these low energies, the distance traveled byneutrons before capturing (order 10 cm) is much smaller thanthe position reconstruction resolution (order 1 m). The
distribution of DC times was assumed to have the formtt
e- tc (1 - e- tt ), where τt is the time taken for the neutron to

5.3. Background Model
The background to the single neutron channel is mostly
events from dark noise and radioactive decays with similar
characteristics to TNCs (“fake neutrons”), and to a lesser extent
events which are real TNCs not from pre-SN neutrinos.
Backgrounds to DC type events include accidental DC between
unrelated background events, real DC for certain radioactive
decays, and real DC from reactor neutrinos and geoneutrinos.
It is anticipated that rates for intrinsic backgrounds will beknown quite precisely once in situ measurements become
possible, but estimates have been made during the planning anddevelopment of SK-Gd. In order to estimate the rate of fakeneutrons intrinsic to SK, events recorded by the WIT system
during 6000 hr of normal pure water data from the fourth run
period of SK (SK-IV) were used. An initial event selection forneutrons was designed to reduce the rate of fake neutrons to areasonable level, while retaining as much efﬁciency for
simulated TNC events as possible. These data were also used
to estimate the accidental DC rate by searching for pairs ofevents in DC.
A large part of the intrinsic background of the SK detector
comes from radon emanation into the water from materials
inside the detector and radioactive decays in the detectormaterials; this is mainly concentrated at the edges and bottomof the detector (Takeuchi et al. 1999; Nakano 2017), and isreduced by ﬁducial volume and energy threshold cuts.
Any radioactive impurities left in the gadolinium sulfate will
be distributed throughout the detector volume by loading.
Much of the work preparing for SK-Gd has been in quantifying
these, including with low background counting using highpurity germanium detectors, and working with chemical
manufacturers to reduce the level of contamination such thatit does not detrimentally impact other SK analyses (Ikeda et al.
2019). In this study, to allow for this additional contamination,
backgrounds calculated from SK-IV data, and assumed backgrounds from (α, n) and spontaneous ﬁssion (SF), are scaled up
by a factor of two in the worst case.
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The Astrophysical Journal, 885:133 (14pp), 2019 November 10

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Figure 6. Distribution of DC channel variables in ﬁnal selection for signal and largest backgrounds. Solid lines show before selection, dotted after. Background isnormalized to 12hr. Signal is normalized to the ﬂux in the ﬁnal 12hr before CCSN for a 25Me star at 500pc.

neutron multiplicity from Santi & Miller (2008), it is assumed
that decays with more than one neutron can also form a DC.
The contribution to the background of SF turns out to be
subdominant in this analysis. Beta delayed neutrons from SF
will be negligible.
Reactor and geo electron antineutrinos are an irreducible
background, being the same particles in the same energy range
as the signal. The reactor background depends strongly on thenumber of Japanese nuclear reactors that are running. To
account for this, the reactor and geo neutrino ﬂux was
calculated using thegeoneutrinos.org web app (Barna &Dye 2015), which combines reference models for reactor
neutrinos (Baldoncini et al. 2015) and geoneutrinos (Huang
et al. 2013). Reactor activity is assumed as the mean values
given by IAEA’s PRIS database for the years 2010 and 2017
(IAEA 2018). During 2010 most Japanese nuclear reactors
were running as normal, however, many were switched off in2011. Some reactors began to be returned to operation after
2010, so 2017 is taken to represent the lowest ﬂux which islikely in the future, and 2010 the highest. Two reactors at
Takahama were restarted in 2017 May and June, so the average

The process 18O (a, n )21Ne*, and its equivalent with 17O,
produce neutrons. There was some concern that this could
produce a high rate of neutrons, especially if there were a highcontamination of 235U series isotopes that are α emitters.
Efforts to reduce radioisotope contamination of gadolinium
sulfate have brought this down to an acceptable predicted level.
As 21Ne* decays by neutron emission, each reaction of thiskind produces two neutrons, so it would be possible for one of
the TNCs to be mistaken for a positron, creating a DC
background. Rates were calculated using the SOURCES code
Wilson et al. (2002). At expected levels of contamination fromthe 238U (<5 mBq kg−1), 235U (<3 mBq kg−1), and 232Th(<0.05 mBq kg−1) chains (Ikeda et al. 2019), we estimate theseprocesses will contribute 6–12 pairs of neutrons per day before
detection efﬁciency. SF of 238U also produces on average morethan one neutron per ﬁssion (Verbeke et al. 2007), andγ-raysthat could be mistaken for positrons, producing a DC
background. Assuming a 238U contamination of the gadolinium
sulfate of 5mBq kg−1, the SF rate is calculated to be 0.6 perhour in the ID before efﬁciencies. The γ-raysare assumed tohave falling energy distribution (Sobel et al. 1973). Using the7The Astrophysical Journal, 885:133 (14pp), 2019 November 10

Simpson et al.

July–December was used for 2017. If Japanese nuclear reactor
activity returns to 2010 levels, then reactor neutrinos will be an
important background to the DC channel of this analysis.
Fast neutrons from cosmic-ray muons are not a concern.
Neutrons produced by muons not passing through the detector
should be few in number; fast neutrons will mostly not
penetrate to the FV before capturing as they would need to passthrough >4.5m of water to do so. Cosmic-ray muons passing
through the detector are detected very efﬁciently; products ofspallation are already rejected at SK by vetoing events
associated in time and space with a muon track (Zhang et al.
2016). Backgrounds from fast neutrons could be controlled bysimply vetoing for 1ms after each muon, which would
introduce negligible dead time.
A fraction of muons create unstable daughter nuclei throughspallation, and those that do can be efﬁciently identiﬁed
through their light emission proﬁles (Li & Beacom 2015a,
2015b). Some unstable isotopes produce β-delayed neutrons,
which can form a DC candidate. Especially the β n decay ofnitrogen-17 has a β-ray energy in the energy range of interest.
This isotope should be efﬁciently identiﬁed by new spallation
reduction methods, so 10% dead time and 95% reduction isassumed, making it small compared to other backgrounds.
Reactor and geo neutrino IBD, spallation daughters decayingby β-delayed neutron, and neutrons from SF and (α, n) are all
evaluated with MC and added to the backgrounds calculated
from pure water data. Remaining backgrounds after cuts arelisted in Section 5.4.

background data, with performance evaluated on the whole
data set. Although the details of the Gd γ-ray cascade models
may be reﬁned when in situ measurements are made using
neutron sources, it is expected that the comparison between thetwo cascade models used is sufﬁcient to cover potential
differences in performance.
BDTs are a multivariate method capable of combining
multiple discriminating variables into a single score. Cutcriteria (decision trees) are automatically generated to separate
signal and background training samples. A weight is assigned
to each tree according to the accuracy of the tree. Events which
are poorly classiﬁed are given boosted weight in subsequent
trees. The overall score comes from the weighted sum of the
response of all decision trees in the ensemble. The TMVA
(Hoecker et al. 2007) software package was used. For a moredetailed, rigorous explanation, see Hoecker et al. (2007) and
Freund & Schapire (1997).
Cuts were optimized to give the greatest range for efﬁcient
detection of a 25Me pre-SN star using the model of
Odrzywolek & Heger (2010) 0.1hr before core collapse,
assuming the more optimistic background and efﬁciency cases.
The distribution of the variables used in the ﬁnal selection of
DC events are shown in Figure 6 for the two largest
backgrounds in the most important variables. While theaccidental backgrounds are well controlled by the combinationof coincidence variables and the neutron BDT, the backgroundfrom pairs of neutrons is harder to reduce. Selection of single
neutrons was based mostly on the BDT classiﬁer, with
distributions in the ﬁnal selection shown in Figure 7. The cuts
shown in Figure 6 are delayed candidate BDT score >0,
coincidence distance <300 cm, and coincidence time <100 μs.
The cut shown in Figure 7 is candidate BDT score >0.56.
Total trigger and selection efﬁciency depends on the energy
distribution of the ﬂux. In the ﬁnal 12hr before core collapse,
the proportion of all IBD events which are triggered andselected is 4.3%–6.7% for DC events, and 9.5%–10% forneutron singles. Using the alternative MC for the γ-raysfrom
TNC on gadolinium, these numbers are 3.9%–6.1% and 7.3%–
8.0% respectively. A range is given as these are ﬂux averaged
efﬁciencies, and therefore depend on the ﬂux spectrum.
Figure 8 shows the total efﬁciency for DC events against thetrue positron energy. The efﬁciency for selecting TNC on Gd
does not depend on the true positron energy. Note that this
efﬁciency curve has been directly optimized for detecting the
pre-SN ﬂux in the ﬁnal 12hr, so should not be used in higher
energy contexts.
The selection requirements on the DC time, and the positron
quality requirements, are very efﬁcient for signal. DC signal
efﬁciency is lower at low energies due to the lower trigger
efﬁciencies, and worse vertex reconstruction leading toincreased DC distance. It should not be alarming that this
efﬁciency is lower and these backgrounds higher than are
typically quoted for other SK analyses (such as the supernova
relic neutrino analysis), which are at higher energy. At higher
energy, reconstruction is much better, and backgrounds much
lower. Almost all prompt events in this analysis reconstructbelow the energy threshold of any other analysis at SK. If the
TNCs were only on H, rather than Gd, there would be nosensitivity at all in this analysis.
Remaining backgrounds for single neutrons, dominated byfake neutrons from the intrinsic radioactivity of the detector,
total 66–140 per 12hr window. For DC events, remaining

5.4. Event Selection
The rate at which WIT recorded data in SK-IV was typicallyon the order of 108 event candidates per day. Most low energy
triggers have a reconstructed position near the detector wall,and follow an exponentially falling energy and hit countdistribution.
Initial cuts on the reconstructed vertex location and numberof hit PMTs are used to select neutron candidates for the single
neutron channel, reducing the background rate by a factor of
104. These cuts are based on the number of on-time hits, qualityof reconstruction, and reconstructed vertex location. This is
47% efﬁcient for simulated TNC on Gd candidates within the
ID. The initial selection was based on the reconstructed vertex
location and number of selected PMT hits, and the distancefrom the reconstructed vertex to the detector wall in thereconstructed direction of the event.
DC events are selected by searching for events close in timethat have been reconstructed within the FV. The time between
the neutron and positron, and the distance between theirreconstructed positions, is shown in Figure 5 for DC signal MC
with 3MeV positrons, and accidental background. Other than
the standard FV cut, selection of positron candidates is kept
very inclusive, in order to achieve the lowest possible neutrinoenergy threshold. The efﬁciencies of these cuts are somewhat
energy dependent due to the deterioration of vertex resolution
at low energy. A cut is also applied to delayed event candidates
based on the distance from the reconstructed vertex to thedetector wall, and the number of selected PMT hits. A boosted
decision tree (BDT) classiﬁer based on the angular distributionof hits, distance from the detector wall, reconstructed energy,
and reconstruction quality is used to select good neutron
candidates for both the DC and single neutron channels. The
BDT was trained on a random 5% subsample of the WIT
8

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Figure 8. Trigger and selection efﬁciency for DC events against prompt
positron true total energy. Above 4MeV positrons are efﬁciently triggered andreconstructed, and so the efﬁciency is determined by the triggering andselection of the delayed part of the event. The result with the alternative Gd γray-cascade model is shown in blue.

Figure 7. Distribution of single neutron channel Gd BDT score in ﬁnal
selection for signal and largest background. Solid lines show the distribution
before selection, the dotted line shows the cut point. Background is normalized
to 12hr. Signal is normalized to the ﬂux in the ﬁnal 12hr before CCSN for a
25Me star at 500pc. The distribution of the Gd BDT score is different fromthat shown in Figure 6 as single neutron events had a higher hit threshold
applied in pre-selection.

Table 1
Antielectron Neutrino Flux Multiplied by IBD Cross Section for Models
Consideredbackgrounds are 5–11 accidental, 0.2–0.4 from SF γ-raysincoincidence with neutrons, 6.8–14 from pairs of neutrons,
0.3–3.0 from reactors; in total 12–28 per 12hr window.

MO

Mass
(Me)

Model

NO

15

Odrzywolek &Heger (2010)
Patton et al. (2017b)
Odrzywolek &Heger (2010)
Patton et al. (2017b)
Odrzywolek &Heger (2010)
Patton et al. (2017b)
Odrzywolek &Heger (2010)
Patton et al. (2017b)

5.5. Detection Strategy
15
25

The simplest way of searching for a rapid excursion in thecandidate event rate is to deﬁne some signal time window (e.g.,
12 hr), and background window (e.g., 30 days), then perform ahypothesis test. The null hypothesis is that the observed rate in
the signal window is consistent with the observed rate in the
background window, taking into account Poissonian ﬂuctuations. The alternative hypothesis is simply that the rate in thesignal window is higher than that in the background window. A
Poisson likelihood for the null hypothesis is calculated for the
total detected event rate, combining both the DC and single
neutron channels.
The time from the start of a data block for reconstruction,
event selection, and hypothesis testing will be around 10
minutes. This sets an estimated typical latency for an alarm.
The length of the signal window should be similar to thetimescale over which the event rate would change if a pre-SN
star was detected. The choice of signal window size can have adramatic effect on the sensitivity of the analysis, as it affects thestatistical ﬂuctuations, the background level, and the trial
factor. A range from 1 to 72 hr were tested and the 12hr signal
window performed best. For the model used, the ne ﬂux
multiplied by the IBD cross section, integrated over the ﬁnal
12hr before collapse is summarized in Table 1.
A longer background window would always be better due toreduced uncertainty in the background rate; however, thebackground rate may change slowly over time. A gradualchange might be expected, due to the gradual increase in PMT
gain, seasonal variations in the radon concentration in the mineair (Nakano et al. 2017; Pronost et al. 2018), changes in waterﬂow affecting radon activity in the ﬁducial volume, or changes
in nearby nuclear power station activity. In this study it isassumed that the background level is known precisely when thesignal is detected.

IO

30
15
15
25
30

ne ﬂux in ﬁnal 12 hr(1012 cm2 per 12 hr per nucleon)
1.6
1.9
3.3
3.8
0.44
0.53
0.93
1.2

These methods are chosen for the purpose of benchmarking
performance. In practice, greater sensitivity can be achieved byproperly accounting for the likelihood of the event rate acrossseparate bins, and by assuming a more complicated alternative
hypothesis, for example, by calculating the rate of increase of
the candidate event rate. Furthermore, a model including
background time variation, measurement uncertainty, andcorrelation between bins, should be included when a large
enough background sample has been collected during SK-Gd.
In attempting to get an SN early warning from the detectionof pre-SN neutrinos, there are four variables which together
describe a detector’s performance.
1. Alarm efﬁciency, i.e., the probability of correctly
detecting a true pre-SN.
2. False positive rate (FPR).
3. Expected time of early warning that the detector would
provide.
4. Expected distance to which the warning would be
efﬁcient.
Tolerating a higher FPR would allow for greater range andmore warning, but would reduce trust in the warning system; a9The Astrophysical Journal, 885:133 (14pp), 2019 November 10

Simpson et al.

Figure 9. Expected maximum range of detection in the ﬁnal 12 hr before collapse, for 15Me and 25Me stars. The width of the bands shows uncertainty due to levelsof background in SK-Gd, and the difference between models of γ-ray emission from TNC on Gd.

choice needs to be made on what FPR is acceptable. Note thatan SN within 1kpc is a rare occurrence—on the order of 1 in
10,000 yr based on historical data (Adams et al. 2013). FPR
levels shown in this paper are 1peryear and 1percentury(cy.), and are assumed to be set by Poisson ﬂuctuations
only. Range is deﬁned as the point at which alarm efﬁciency is50%. By formulating the problem in terms of FPR, a trials
factor is incorporated.

emission from TNC on Gd. The distances at which alarm
efﬁciency is above 50% are summarized in Table 2.
Results depend on the neutrino mass ordering (normal NO or
inverted IO), the ZAMS mass of the star, the distance to thestar, and the background level in SK-Gd. Questions of detector
model uncertainty will be resolved by in situ measurements
once SK-Gd is loaded. An inverted neutrino mass ordering is
detrimental to this analysis as it reduces the ne fraction of the
pre-SN ﬂux.
Discussions of pre-SN stars often focus on α-Ori (Betelgeuse) as an example of a nearby massive star, although α-Sco(Antares) has a similar mass and distance. For the purpose ofthis study α-Ori is assumed to be 200pc from Earth, with amass between 15and25Me. Estimates of Betelgeuse’s mass
are correlated with its distance (Dolan et al. 2016), so two
extremes chosen for benchmarking performance are that it is150pc away and 15Me, or 250pc away and 25Me. These

6. Results
Figure 9 shows the distance to the pre-SN star at which thenull hypothesis would be rejected before core collapse.
Figure 10 shows the largest amount of time before corecollapse at which a pre-SN is expected to be detected. The
width of the bands shows uncertainty due to levels of
background and the difference between models of γ-ray
10The Astrophysical Journal, 885:133 (14pp), 2019 November 10

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Figure 10. Expected early warning against range of detection, for 15Me and 25Me stars. It is assumed the range is the largest distance at which probability ofdetection is greater than 50%.
Table 2
Maximum Range of Detection under Various Assumptions

NO

IO

Mass
(Me)
15
15
25
30
15
15
25
30

Model

Max. Range (pc)
given FPR
1 yr−1

1/cy.

Odrzywolek
PattonOdrzywolek
PattonOdrzywolek
PattonOdrzywolek
Patton300–400
420–600
330–400
480–600
160–200
220–300
180–200
270–400

250–300
360–500
280–400
410–500
130–200
190–200
150–200
230–300of detected events at SK-Gd under these assumptions, after
detection efﬁciencies are taken into account. The model with
30Me is also included for the sake of comparison. The
expected number of detected events in the ﬁnal 12hr before
collapse are summarized in Table 3, and the expected amountof warning is summarized in Table 4.
The KamLAND collaboration published an analysis of theirown sensitivity to pre-SN neutrinos (Asakura et al. 2016),
which is compared to the expected sensitivity of SK-Gd.
KamLAND is a liquid scintillator detector based in the samemine as SK. It has lower energy thresholds than SK, and sowould detect IBD events from a pre-SN more efﬁciently, andhas lower background rates. However, the mass of SK is morethan 20 times larger than that of KamLAND, so more events
are seen in total.
The nominal performance of KamLAND is taken from
Asakura et al. (2016). KamLAND background rates are
0.071–0.355 events per day depending on Japanese nuclear
reactor power. Events are integrated over a 48hr sliding
window every 15minutes. Assumed signal rates in the ﬁnal

Note.Range is assumed to be the highest distance at which alarm efﬁciency isgreater than 50%. Uncertainty comes from the TNC γ-ray model, andbackground uncertainty. The same information is contained in Figure 9.

values are chosen for comparison to Asakura et al. (2016),
rather than to match the most up-to-date precise estimates of αOriʼs distance and mass. Figure 11 shows the expected number
11

The Astrophysical Journal, 885:133 (14pp), 2019 November 10

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Figure 11. Expected signal events in a 12hr time window, after detection efﬁciencies are taken into account as per Section 5. A distance of 200pc and NO isassumed. Dotted lines show the high and low background assumptions.

Table 3
Expected Numbers of Events at 200kpc in the Final 12hr before Collapse at
SK-Gd

NO

IO

Model

Mass
(Me)

SingleNeutronsDC

Odrzywolek
PattonOdrzywolek
PattonOdrzywolek
PattonOdrzywolek
Patton

15
15
25
30
15
15
25
30

55–71
65–84
120–160
130–170
16–20
18–23
34–44
40–52

33–36
45–50
59–65
100–110
9–10
13–15
17–18
34–37

Table 4
Time at Which Expected Signal Exceeds Threshold, for Some Assumptions
Chosen to Represent Betelgeuse

Model

AssumedDistance(pc)

Warning (hr)Given FPR
1 yr−1

1/cy.

150
150
250
250
150
150
250
250

Odrzywolek
PattonOdrzywolek
PattonOdrzywolek
PattonOdrzywolek
Patton5.3–8.4
7.1–14.1
4.7–7.4
1.0–1.6
0.1–2.0
0.3–4.1
0.0–0.6
0.1–0.4

3.4–6.3
5.1–9.8
3.3–5.7
0.7–1.1
0.0–0.8
0.0–2.2
0.0–0.0
0.0–0.1

MO

NO

IO

Mass
(Me)
15
15
25
30
15
15
25
30

Note.Uncertainty comes from TNC γ-ray model only.
Note.30 Me at 250pc is far from the range of mass estimates for Betelgeuse,but is provided anyway for comparison. Uncertainty comes from the TNC γray model, and background uncertainty.

48hr at 200pc are 25.7(7.28) in the 25Me case, 12.0(3.38) inthe 15Me case, for the NO(IO) case. The pre-SN models usedwere those of Odrzywolek & Heger (2010). Not enoughinformation is provided in Asakura et al. (2016) to directly andfairly compare warning times.
Figure 12 shows the probability of detection before corecollapse (t=0) against distance to the pre-SN star. The
estimated range for KamLAND is also shown. KamLAND has
a latency of 25minutes, which is not taken into account. The
FPR is set to match that of the 3σ and 5σ with a 48hr signal
window used by KamLAND, for the sake of comparison. That
1
is, the false positive rate is set to 370 per48hr for 3σ and7. Conclusion
Electron antineutrinos from a pre-SN star precede those froma CCSN by hours or days, increasing in ﬂux and energy rapidly
over a period of hours: this has never been detected. In the nextstage of SK, gadolinium loading will enable efﬁcient
identiﬁcation of neutrons, enabling the reduction in the energy
threshold for the detection of ne .
The background rates and signal efﬁciencies for an SK-Gd
low energy analysis capable of detecting pre-SN ne have been
quantiﬁed. This requires detection of events below the usualenergy thresholds of SK, for which trigger efﬁciency andreconstruction are poorer, and backgrounds higher. Gadolinium
loading is essential to detecting these events. Through a rapid
increase in the number of event candidates, additional warningof a very nearby SN can be achieved, and useful information
provided about late stellar burning processes that lead up to asupernova.
Based on this and the predicted ﬂuxes of Odrzywolek &Heger (2010) and Patton et al. (2017b), estimates were1
1744278

per48hr for 5σ. By this comparison, the maximum
detection range of SK-Gd is slightly shorter than that of
KamLAND. This is due to KamLAND’s lower expectedbackground rate.
Next generation liquid scintillator and Gd-loaded water
Cerenkov detectors could provide earlier warning to longer
distances due to their large target masses. A future dark-matterdirect-detection experiment could also detect signiﬁcantnumbers of pre-SN neutrinos through coherent scattering, withthe advantage of being sensitive to all ﬂavors (Raj et al. 2019).
12

The Astrophysical Journal, 885:133 (14pp), 2019 November 10

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Figure 12. As in Figure 9, but with reference numbers for KamLAND included. The FPR is ﬁxed at the equivalent of 5σ/3σ with a trial factor of 1per48hr.
Differences in expected alarm latency are not taken into account. Pre-SN models shown are those of Odrzywolek et al.

false positive rate of 1peryear, 5–14hr of early warning could
be achieved for α-Ori, and maximum detection range could
extend to 600pc.
A pre-SN alert could be provided by SK to the astrophysics
community following gadolinium loading. Future large neutrino detectors will improve the potential range of detection,
especially if they have a sufﬁciently small low energy threshold
and the ability to tag neutrons from IBD. It could also be
possible to use multiple detectors in combination to provide a
pre-SN alert with higher conﬁdence.

produced of the distance at which a pre-SN star could beobserved, and the amount of additional early warning that
could be expected. Uncertainty in the future capabilities of thedetector arises mainly from the future internal contamination of
the SK detector, which is the main source of backgrounds at
low energy. This uncertainty will be reduced once in situ
measurements of background become available. An inverted
neutrino mass ordering would have a detrimental effect on therange of this technique by reducing the ne fraction of the n ﬂux
leaving the star.
The nearest RSG star to Earth is α-Ori, which we assume tohave an initial mass of 15–25 Me and distance from Earth of
150–250pc. Assuming normal neutrino mass ordering, 0.2%
gadolinium sulfate loading at SK, α-Ori going pre-SN could
lead to the detection of more than 200 events in SK-Gd in theﬁnal 12hr before core collapse, well exceeding the expectedbackground. Assuming a statistical false positive rate of1percentury, if it were pre-SN, α-Ori could be detected
3–10hr before core collapse, and the greatest distance at whicha pre-SN star could be detected is 500pc. Allowing a higher

We gratefully acknowledge the cooperation of the Kamioka
Mining and Smelting Company. The SuperKamiokande
experiment has been built and operated from funding by theJapanese Ministry of Education, Culture, Sports, Science andTechnology, the U.S. Department of Energy, and the U.S.
National Science Foundation. Some of us have been supported
by funds from the National Research Foundation of Korea
NRF20090083526 (KNRC) funded by the Ministry of Science,
ICT, and Future Planning and the Ministry of Education
13

The Astrophysical Journal, 885:133 (14pp), 2019 November 10

Simpson et al.

(2018R1D1A3B07050696, 2018R1D1A1B07049158), theJapan Society for the Promotion of Science, the National
Natural Science Foundation of China under grants No.
11235006, the National Science and Engineering ResearchCouncil (NSERC) of Canada, the Scinet and Westgrid
consortia of Compute Canada, the National Science Centre,
Poland (2015/18/E/ST2/00758), the Science and Technology
Facilities Council (STFC) and GridPP, UK, and the European
Unions H2020-MSCA-RISE-2018 JENNIFER2 grant agreement no.822070.

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ORCID iDs
C. Simpson https://orcid.org/0000-0001-9356-5833
K. Ohta https://orcid.org/0000-0003-3844-1517
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