# Pre-Supernova Alert System for Super-Kamiokande

L. N. Machado  Dipartimento di Fisica, INFN Sezione di Napoli and Università
di Napoli, I-80126, Napoli, Italy  K. Abe  Kamioka Observatory, Institute for
Cosmic Ray Research, University of Tokyo, Kamioka, Gifu 506-1205, Japan  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  Y. Hayato  Kamioka Observatory, Institute for
Cosmic Ray Research, University of Tokyo, Kamioka, Gifu 506-1205, Japan  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  K. Hiraide  Kamioka Observatory, Institute for
Cosmic Ray Research, University of Tokyo, Kamioka, Gifu 506-1205, Japan  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  K. Ieki  Kamioka Observatory, Institute for
Cosmic Ray Research, University of Tokyo, Kamioka, Gifu 506-1205, Japan  M.
Ikeda  Kamioka Observatory, Institute for Cosmic Ray Research, University of
Tokyo, Kamioka, Gifu 506-1205, Japan  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  J. Kameda
Kamioka Observatory, Institute for Cosmic Ray Research, University of Tokyo,
Kamioka, Gifu 506-1205, Japan  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  Y. Kanemura  Kamioka
Observatory, Institute for Cosmic Ray Research, University of Tokyo, Kamioka,
Gifu 506-1205, Japan  R.. Kaneshima  Kamioka Observatory, Institute for Cosmic
Ray Research, University of Tokyo, Kamioka, Gifu 506-1205, Japan  Y. Kashiwagi
Kamioka Observatory, Institute for Cosmic Ray Research, University of Tokyo,
Kamioka, Gifu 506-1205, Japan  Y. Kataoka  Kamioka Observatory, Institute for
Cosmic Ray Research, University of Tokyo, Kamioka, Gifu 506-1205, Japan  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  S. Miki  Kamioka Observatory, Institute for
Cosmic Ray Research, University of Tokyo, Kamioka, Gifu 506-1205, Japan  S.
Mine  Kamioka Observatory, Institute for Cosmic Ray Research, University of
Tokyo, Kamioka, Gifu 506-1205, Japan  M. Miura  Kamioka Observatory, Institute
for Cosmic Ray Research, University of Tokyo, Kamioka, Gifu 506-1205, Japan
S. Moriyama  Kamioka Observatory, Institute for Cosmic Ray Research,
University of Tokyo, Kamioka, Gifu 506-1205, Japan  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  Y. Nakano  Kamioka Observatory, Institute for Cosmic Ray Research,
University of Tokyo, Kamioka, Gifu 506-1205, Japan  M. Nakahata  Kamioka
Observatory, Institute for Cosmic Ray Research, University of Tokyo, Kamioka,
Gifu 506-1205, Japan  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  S. Nakayama  Kamioka
Observatory, Institute for Cosmic Ray Research, University of Tokyo, Kamioka,
Gifu 506-1205, Japan  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  Y. Noguchi  Kamioka
Observatory, Institute for Cosmic Ray Research, University of Tokyo, Kamioka,
Gifu 506-1205, Japan  K. Okamoto  K. Sato  Kamioka Observatory, Institute for
Cosmic Ray Research, University of Tokyo, Kamioka, Gifu 506-1205, Japan  H.
Sekiya  Kamioka Observatory, Institute for Cosmic Ray Research, University of
Tokyo, Kamioka, Gifu 506-1205, Japan  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  H. Shiba
Kamioka Observatory, Institute for Cosmic Ray Research, University of Tokyo,
Kamioka, Gifu 506-1205, Japan  K. Shimizu  Kamioka Observatory, Institute for
Cosmic Ray Research, University of Tokyo, Kamioka, Gifu 506-1205, Japan  M.
Shiozawa  Kamioka Observatory, Institute for Cosmic Ray Research, University
of Tokyo, Kamioka, Gifu 506-1205, Japan  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  Y. Sonoda
Y. Suzuki  Kamioka Observatory, Institute for Cosmic Ray Research, University
of Tokyo, Kamioka, Gifu 506-1205, Japan  A. Takeda  Kamioka Observatory,
Institute for Cosmic Ray Research, University of Tokyo, Kamioka, Gifu
506-1205, Japan  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  Y. Takemoto  Kamioka
Observatory, Institute for Cosmic Ray Research, University of Tokyo, Kamioka,
Gifu 506-1205, Japan  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  A. Takenaka  Kamioka
Observatory, Institute for Cosmic Ray Research, University of Tokyo, Kamioka,
Gifu 506-1205, Japan  H. Tanaka  Kamioka Observatory, Institute for Cosmic Ray
Research, University of Tokyo, Kamioka, Gifu 506-1205, Japan  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  S. Watanabe  Kamioka Observatory, Institute for Cosmic Ray Research,
University of Tokyo, Kamioka, Gifu 506-1205, Japan  T. Yano  Kamioka
Observatory, Institute for Cosmic Ray Research, University of Tokyo, Kamioka,
Gifu 506-1205, Japan  P. de Perio  K. Martens  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  M. R.
Vagins  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  Department of Physics and Astronomy,
University of California, Irvine, Irvine, CA 92697-4575, USA  J. Bian  N. J.
Griskevich  Department of Physics and Astronomy, University of California,
Irvine, Irvine, CA 92697-4575, USA  W. R. Kropp  Deceased.  Department of
Physics and Astronomy, University of California, Irvine, Irvine, CA
92697-4575, USA  S. Locke  Department of Physics and Astronomy, University of
California, Irvine, Irvine, CA 92697-4575, USA  M. B. Smy  H. W. Sobel
Department of Physics and Astronomy, University of California, Irvine, Irvine,
CA 92697-4575, USA  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  V. Takhistov  Department
of Physics and Astronomy, University of California, Irvine, Irvine, CA
92697-4575, USA  A. Yankelevich  Department of Physics and Astronomy,
University of California, Irvine, Irvine, CA 92697-4575, USA  S. Han  Research
Center for Cosmic Neutrinos, Institute for Cosmic Ray Research, University of
Tokyo, Kashiwa, Chiba 277-8582, Japan  T. Kajita  Research Center for Cosmic
Neutrinos, Institute for Cosmic Ray Research, University of Tokyo, Kashiwa,
Chiba 277-8582, Japan  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  K. Okumura  Research
Center for Cosmic Neutrinos, Institute for Cosmic Ray Research, University of
Tokyo, Kashiwa, Chiba 277-8582, Japan  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  T.
Tashiro  T. Tomiya  X. Wang  J. Xia  S. Yoshida  Research Center for Cosmic
Neutrinos, Institute for Cosmic Ray Research, University of Tokyo, Kashiwa,
Chiba 277-8582, Japan  G. D. Megias  Institute for Cosmic Ray Research,
University of Tokyo, Kashiwa, Chiba 277-8582, Japan  P. Fernandez  L. Labarga
N. Ospina  B. Zaldivar  Department of Theoretical Physics, University Autonoma
Madrid, 28049 Madrid, Spain  B. W. Pointon  Department of Physics, British
Columbia Institute of Technology, Burnaby, BC, V5G 3H2, Canada  TRIUMF, 4004
Wesbrook Mall, Vancouver, BC, V6T2A3, Canada  R. Akutsu  V. Gousy-Leblanc
also at University of Victoria, Department of Physics and Astronomy, PO Box
1700 STN CSC, Victoria, BC V8W 2Y2, Canada.  TRIUMF, 4004 Wesbrook Mall,
Vancouver, BC, V6T2A3, Canada  M. Hartz  TRIUMF, 4004 Wesbrook Mall,
Vancouver, BC, V6T2A3, Canada  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  A. Konaka  N. W. Prouse
TRIUMF, 4004 Wesbrook Mall, Vancouver, BC, V6T2A3, Canada  E. Kearns
Department of Physics, Boston University, Boston, MA 02215, USA  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  J. L. Raaf  Department of Physics, Boston
University, Boston, MA 02215, USA  L. Wan  Department of Physics, Boston
University, Boston, MA 02215, USA  T. Wester  Department of Physics, Boston
University, Boston, MA 02215, USA  J. Hill  Department of Physics, California
State University, Dominguez Hills, Carson, CA 90747, USA  J. Y. Kim  I. T. Lim
R. G. Park  Institute for Universe and Elementary Particles, Chonnam National
University, Gwangju 61186, Korea  B. Bodur  Department of Physics, Duke
University, Durham NC 27708, USA  K. Scholberg  C. W. Walter  Department of
Physics, Duke University, Durham NC 27708, USA  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  L. Bernard  A. Coffani  O. Drapier  S. El Hedri  A. Giampaolo  Th. A.
Mueller  A. D. Santos  P. Paganini  B. Quilain  Ecole Polytechnique,
IN2P3-CNRS, Laboratoire Leprince-Ringuet, F-91120 Palaiseau, France  T.
Ishizuka  Junior College, Fukuoka Institute of Technology, Fukuoka, Fukuoka
811-0295, Japan  T. Nakamura  Department of Physics, Gifu University, Gifu,
Gifu 501-1193, Japan  J. S. Jang  GIST College, Gwangju Institute of Science
and Technology, Gwangju 500-712, Korea  J. G. Learned  Department of Physics
and Astronomy, University of Hawaii, Honolulu, HI 96822, USA  S. Cao
Institute For Interdisciplinary Research in Science and Education, ICISE, Quy
Nhon, 55121, Vietnam  K. Choi  Institute for Basic Science, Yuseong-gu,
Daejeon, Korea  L. H. V. Anthony  D. Martin  M. Scott  A. A. Sztuc  Y. Uchida
Department of Physics, Imperial College London , London, SW7 2AZ, United
Kingdom  V. Berardi  M. G. Catanesi  E. Radicioni  Dipartimento
Interuniversitario di Fisica, INFN Sezione di Bari and Università e
Politecnico di Bari, I-70125, Bari, Italy  N. F. Calabria  G. De Rosa
Dipartimento di Fisica, INFN Sezione di Napoli and Università di Napoli,
I-80126, Napoli, Italy  G. Collazuol  F. Iacob  M. Lamoureux  M. Mattiazzi
Dipartimento di Fisica, INFN Sezione di Padova and Università di Padova,
I-35131, Padova, Italy  L. Ludovici  INFN Sezione di Roma and Università di
Roma “La Sapienza”, I-00185, Roma, Italy  M. Gonin  International Laboratory
for Astrophysics, Neutrino and Cosmology Experiment, Kashiwa, Chiba 277-8582,
Japan  G. Pronost  International Laboratory for Astrophysics, Neutrino and
Cosmology Experiment, Kashiwa, Chiba 277-8582, Japan  C. Fujisawa  Y. Maekawa
Y. Nishimura  R. Sasaki  Department of Physics, Keio University, Yokohama,
Kanagawa, 223-8522, Japan  M. Friend  T. Hasegawa  T. Ishida  M. Jakkapu  T.
Kobayashi  T. Matsubara  T. Nakadaira  High Energy Accelerator Research
Organization (KEK), Tsukuba, Ibaraki 305-0801, Japan  K. Nakamura  High Energy
Accelerator Research Organization (KEK), Tsukuba, Ibaraki 305-0801, Japan
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  Y. Oyama  K. Sakashita  T. Sekiguchi  T.
Tsukamoto  High Energy Accelerator Research Organization (KEK), Tsukuba,
Ibaraki 305-0801, Japan  T. Boschi  F. Di Lodovico  J. Gao  A. Goldsack  T.
Katori  J. Migenda  M. Taani  Department of Physics, King’s College London,
London, WC2R 2LS, UK  S. Zsoldos  Department of Physics, King’s College
London, London, WC2R 2LS, 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  Y. Kotsar  H. Ozaki  A.
T. Suzuki  Department of Physics, Kobe University, Kobe, Hyogo 657-8501, Japan
Y. Takeuchi  Department of Physics, Kobe University, Kobe, Hyogo 657-8501,
Japan  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  S. Yaamoto  Department of Physics, Kobe
University, Kobe, Hyogo 657-8501, Japan  C. Bronner  Department of Physics,
Kyoto University, Kyoto, Kyoto 606-8502, Japan  J. Feng  T. Kikawa  M. Mori
Department of Physics, Kyoto University, Kyoto, Kyoto 606-8502, Japan  T.
Nakaya  Department of Physics, Kyoto University, Kyoto, Kyoto 606-8502, Japan
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  R. A. Wendell  Department of Physics, Kyoto
University, Kyoto, Kyoto 606-8502, Japan  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  K.
Yasutome  Department of Physics, Kyoto University, Kyoto, Kyoto 606-8502,
Japan  S. J. Jenkins  N. McCauley  P. Mehta  K. M. Tsui  Department of
Physics, University of Liverpool, Liverpool, L69 7ZE, United Kingdom  Y.
Fukuda  Department of Physics, Miyagi University of Education, Sendai, Miyagi
980-0845, Japan  Y. Itow  Institute for Space-Earth Environmental Research,
Nagoya University, Nagoya, Aichi 464-8602, Japan  Kobayashi-Maskawa Institute
for the Origin of Particles and the Universe, Nagoya University, Nagoya, Aichi
464-8602, Japan  H. Menjo  Institute for Space-Earth Environmental Research,
Nagoya University, Nagoya, Aichi 464-8602, Japan  K. Ninomiya  Institute for
Space-Earth Environmental Research, Nagoya University, Nagoya, Aichi 464-8602,
Japan  J. Lagoda  S. M. Lakshmi  M. Mandal  P. Mijakowski  Y. S. Prabhu  J.
Zalipska  National Centre For Nuclear Research, 02-093 Warsaw, Poland  M. Jia
J. Jiang  C. K. Jung  M. J. Wilking  C. Yanagisawa  also at BMCC/CUNY, Science
Department, New York, New York, 1007, USA.  Department of Physics and
Astronomy, State University of New York at Stony Brook, NY 11794-3800, USA  M.
Harada  H. Ishino  S. Ito  H. Kitagawa  Department of Physics, Okayama
University, Okayama, Okayama 700-8530, Japan  Y. Koshio  Department of
Physics, Okayama University, Okayama, Okayama 700-8530, Japan  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  W. Ma  Department of Physics, Okayama University, Okayama, Okayama
700-8530, Japan  F. Nakanishi  S. Sakai  Department of Physics, Okayama
University, Okayama, Okayama 700-8530, Japan  G. Barr  D. Barrow  Department
of Physics, Oxford University, Oxford, OX1 3PU, United Kingdom  L. Cook
Department of Physics, Oxford University, Oxford, OX1 3PU, United Kingdom
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  S. Samani  Department of Physics, Oxford
University, Oxford, OX1 3PU, United Kingdom  D. Wark  Department of Physics,
Oxford University, Oxford, OX1 3PU, United Kingdom  STFC, Rutherford Appleton
Laboratory, Harwell Oxford, and Daresbury Laboratory, Warrington, OX11 0QX,
United Kingdom  F. Nova  Rutherford Appleton Laboratory, Harwell, Oxford, OX11
0QX, UK  J. Y. Yang  Department of Physics, Seoul National University, Seoul
151-742, Korea  M. Malek  J. M. McElwee  O. Stone  M. D. Thiesse  L. F.
Thompson  Department of Physics and Astronomy, University of Sheffield, S3
7RH, Sheffield, United Kingdom  H. Okazawa  Department of Informatics in
Social Welfare, Shizuoka University of Welfare, Yaizu, Shizuoka, 425-8611,
Japan  S. B. Kim  J. W. Seo  I. Yu  Department of Physics, Sungkyunkwan
University, Suwon 440-746, Korea  A. K. Ichikawa  K. D. Nakamura  S. Tairafune
Department of Physics, Faculty of Science, Tohoku University, Sendai, Miyagi,
980-8578, Japan  K. Nishijima  Department of Physics, Tokai University,
Hiratsuka, Kanagawa 259-1292, Japan  K. Iwamoto  K. Nakagiri  Department of
Physics, University of Tokyo, Bunkyo, Tokyo 113-0033, Japan  Y. Nakajima
Department of Physics, University of Tokyo, Bunkyo, Tokyo 113-0033, Japan
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  N. Taniuchi  Department of Physics, University
of Tokyo, Bunkyo, Tokyo 113-0033, Japan  M. Yokoyama  Department of Physics,
University of Tokyo, Bunkyo, Tokyo 113-0033, Japan  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  S. Izumiyama  M. Kuze  Department of Physics,Tokyo Institute of
Technology, Meguro, Tokyo 152-8551, Japan  M. Inomoto  M. Ishitsuka  H. Ito
T. Kinoshita  R. Matsumoto  Y. Ommura  N. Shigeta  M. Shinoki  T. Suganuma  M.
Yonenaga  Department of Physics, Faculty of Science and Technology, Tokyo
University of Science, Noda, Chiba 278-8510, Japan  J. F. Martin  H. A. Tanaka
T. Towstego  Department of Physics, University of Toronto, ON, M5S 1A7, Canada
S. Chen  B. D. Xu  B. Zhang  Department of Engineering Physics, Tsinghua
University, Beijing, 100084, China  M. Posiadala-Zezula  Faculty of Physics,
University of Warsaw, Warsaw, 02-093, Poland  D. Hadley  M. Nicholson  M.
O’Flaherty  B. Richards  Department of Physics, University of Warwick,
Coventry, CV4 7AL, UK  A. Ali  Department of Physics, University of Winnipeg,
MB R3J 3L8, Canada  TRIUMF, 4004 Wesbrook Mall, Vancouver, BC, V6T2A3, Canada
B. Jamieson  Department of Physics, University of Winnipeg, MB R3J 3L8, Canada
Ll. Marti  A. Minamino  G. Pintaudi  S. Sano  S. Suzuki  K. Wada  Department
of Physics, Yokohama National University, Yokohama, Kanagawa, 240-8501, Japan

###### Abstract

In 2020, the Super-Kamiokande (SK) experiment moved to a new stage (SK-Gd) in
which gadolinium (Gd) sulfate octahydrate was added to the water in the
detector, enhancing the efficiency to detect thermal neutrons and consequently
improving the sensitivity to low energy electron anti-neutrinos from inverse
beta decay (IBD) interactions. SK-Gd has the potential to provide early alerts
of incipient core-collapse supernovae through detection of electron anti-
neutrinos from thermal and nuclear processes responsible for the cooling of
massive stars before the gravitational collapse of their cores. These pre-
supernova neutrinos emitted during the silicon burning phase can exceed the
energy threshold for IBD reactions. We present the sensitivity of SK-Gd to
pre-supernova stars and the techniques used for the development of a pre-
supernova alarm based on the detection of these neutrinos in SK, as well as
prospects for future SK-Gd phases with higher concentrations of Gd. For the
current SK-Gd phase, high-confidence alerts for Betelgeuse could be issued up
to nine hours in advance of the core-collapse itself.

Astroparticle physics — Neutrinos — Supernova

##  1 Introduction

The first observation of neutrinos produced outside of our solar system
happened on February 23rd, 1987, due to a neutrino burst from a supernova
explosion known as SN1987A in the Large Magellanic Cloud, located
approximately 50 kiloparsecs (163,000 light years) from Earth. While this
supernova – the first to be observed in the vicinity of our own galaxy since
the invention of the telescope – was initially detected via its emitted
visible light, signals from the corresponding neutrino burst were found in
recorded data to have arrived several hours earlier (Hirata et al., 1988;
Haines et al., 1988; Alexeyev et al., 1988; Aglietta et al., 1987); the
neutrinos are generated before the majority of the electromagnetic radiation.
SN1987A remains the only observation of supernova neutrinos in history. Since
then, a variety of more modern experiments with such advantages as greater
longevity, increased target mass, lower energy threshold, and improved energy
and timing resolution have come online (e.g. Super-Kamiokande (Fukuda et al.,
2003a), IceCube (Aartsen et al., 2017), KamLAND (Eguchi et al., 2003), SNO+
(Albanese et al., 2021), etc.), each hoping to efficiently detect supernova
neutrino bursts and possibly provide early alerts for the delayed
electromagnetic signal.

The Super-Kamiokande (SK) experiment (Fukuda et al., 2003a), located in the
Kamioka mine in Japan, has the potential of detecting low energy (above a few
MeV) neutrino bursts from supernova explosions in our galaxy and its
surroundings. The neutrino elastic scattering interaction is a subdominant
(around 3% of all events) but important interaction channel for low energy
neutrinos in SK, allowing the detector supernova pointing capability by
reconstructing the direction of scattered electrons. SK is currently the
largest and most sensitive SN neutrino detector in the world and it has an
active real-time supernova neutrino burst monitor (Abe et al., 2016). In 2020,
the water in the SK detector was loaded with gadolinium (Gd) to increase the
project’s sensitivity to low energy electron anti-neutrinos ($\bar{\nu}_{e}$)
by improving the identification of neutrons resulting from their most likely
(about 85%) interaction in water: inverse beta decay (IBD).

But not only supernova bursts can provide alerts for supernova explosions.
Prior to the collapse of their cores, very massive stars nearing the end of
their lives are supported by the nuclear fusion of heavy nuclei. These are
commonly known as pre-supernova stars; their main cooling mechanism is via
neutrino emission. Pairs of neutrinos and anti-neutrinos of all flavors are
produced during this phase (Odrzywolek et al., 2004), and the enhanced
sensitivity to low energy $\bar{\nu}_{e}$ in SK-Gd allows the detection of
neutrinos emitted during the terminal silicon (Si) burning period.

A potential world-first detection of pre-supernova neutrinos by SK-Gd would
help to resolve open questions regarding stellar evolution models as well as
provide valuable input regarding the longstanding issue of the neutrino mass
hierarchy (Kato et al., 2020). These new detection capabilities have also
motivated the creation of a pre-supernova alert system for the experiment. SK-
Gd’s sensitivity to pre-supernova neutrinos and details of its new alert
system are presented in this article.

##  2 The Super-Kamiokande Experiment

The SK experiment is a 50 kiloton (kt) water Cherenkov detector located in the
Kamioka mine in Japan (Fukuda et al., 2003b). It was designed to study
neutrinos with energies from a few MeV to few hundreds of GeV, produced by a
variety of both natural and artificial sources. The detector consists of a
cylindrical stainless steel tank with a 39.3 meter diameter and a 41.4 m
height, divided into an inner detector (ID) and an outer detector (OD). The ID
is responsible for the event detection, with over 11,000 20-inch
photomultiplier tubes (PMTs) and it has a volume of 32 kt, although the usual
fiducial volume (FV) used in SK analyses is 22 5 kt. The OD has a thickness of
about 2 m and it is composed of 1,885 8-inch PMTs, facing the outside of the
detector to reduce entering cosmic ray-induced backgrounds.

From July 14th, 2020, SK officially started the SK-Gd phase, in which 13 tons
of gadolinium sulfate octahydrate $\rm Gd_{2}(\rm SO_{4})_{3}\cdot\rm 8H_{2}O$
was added to the water in the detector, achieving a concentration of 0.01% Gd
by mass (Abe et al., 2022). Gadolinium sulfate octahydrate is an easily
soluble compound and it has good transparency to the Cherenkov light. The SK-
Gd project was first proposed as GADZOOKS! by (Beacom & Vagins, 2004) to
improve the identification of neutrons resulting from processes including
inverse beta decay (IBD), the main interaction channel for low energy
$\bar{\nu}_{e}$. IBD interactions have in their final state a prompt positron,
which is directly detected through Cherenkov radiation, and a delayed neutron,
which travels a short distance before being thermalized in water and then
captured by nuclei. These nuclei de-excite by emitting $\gamma$-rays that are
detected mainly due to Compton-scattered electrons producing Cherenkov light.
Among all the natural occurring elements, Gd has the largest thermal neutron
cross section capture, of approximately 49,000 barns.

The de-excitation $\gamma$-rays have different energies depending on the
capture nuclei. A neutron capture on hydrogen emits a single 2.2 MeV
$\gamma$-ray, which has low detection efficiency since its Compton electrons
are close to or below the Cherenkov threshold; in pure water the neutron
capture time constant is approximately $200$ ${\mu}s$. With small amounts
($\geq$ 0.01% by mass) of gadolinium dissolved in the water of the detector,
the majority of thermal neutron captures will be on the Gd since it has a
neutron capture cross section about 100,000 times larger than that of
hydrogen. These captures on Gd emit easily detectable $\gamma$-ray cascades of
about 8 MeV (Beacom & Vagins, 2004). The current phase of SK-Gd corresponds to
a loading of 0.01% Gd by mass and, as a consequence, approximately 50% of the
neutron captures are on Gd. The presence of Gd shortens the neutron capture
time constant, which for the present loading has been measured to be $115\pm
1$ ${\mu}s$ (Abe et al., 2022). In the next loading phase – planned to begin
in mid-2022 – the Gd concentration will be increased to 0.03% to yield
$\sim$75% captures on Gd. The final goal is to have SK loaded with 0.1% Gd,
which will result in approximately 90% of the captures being on Gd.

The search for IBD events is characterized by the coincidence between the
prompt positrons and delayed neutrons. This signature is used to distinguish
background events, since it is unlikely that uncorrelated events produce the
same pattern. The efficient neutron identification provided by gadolinium
brings benefits to different analyses in SK such as supernova neutrinos,
proton decay and atmospheric neutrino studies, and in the search for signals
from nuclear power reactors and the Diffuse Supernova Neutrino Background
(DSNB), which is the as-yet unobserved (Beacom & Vagins, 2004; Beacom, 2010)
integrated neutrino flux from all core-collapse supernova (CCSN) throughout
the history of the universe. Another possible first-time detection of SK-Gd
would be the neutrinos coming from the silicon (Si) burning phase of stars
preceding the CCSN, also known as pre-supernova neutrinos.

The EGADS (Evaluating Gadolinium’s Action on Detector Systems) detector has
been used to simulate the Gd loading in SK (Marti et al., 2020). Also located
in the Kamioka mine, EGADS was designed to be a smaller prototype of SK-Gd; it
is a stainless steel tank with 200 tons of water and 240 PMTs installed on the
walls, and uses the same electronics as SK. Among other important results,
prior to the loading of gadolinium into SK’s water EGADS demonstrated the
ability to maintain ultrapure water-style transparency in the presence of Gd,
a viable method of safely removing the Gd from the detector when desired, and
the additions to the existing SK water system needed in order to prepare for
Gd loading. The EGADS detector is currently running with a concentration of
0.03% Gd by mass in order to validate operating conditions and techniques for
the next planned SK-Gd phase.

##  3 Pre-Supernova Neutrinos

Massive stars, which are stars with initial masses greater than 8 solar masses
($M_{\odot}$) at the Zero Age Main Sequence (ZAMS), often111Alternatively,
massive stars may collapse directly to a black hole with no supernova
explosion (O'Connor & Ott, 2011). end their lives in a CCSN. Prior to their
cores collapsing – as massive stars burn most of their hydrogen into helium –
the increasing density and temperature of their cores allow the fusion of
heavier nuclei (Woosley et al., 1978). With the ignition of carbon burning,
massive stars are classified as neutrino-cooled stars (Arnett, 2020); in this
phase the amount of neutrino emission from thermal and nuclear processes
increases significantly, becoming the main cooling mechanism of these stars. A
neutrino-cooled star lasts hundreds of years, reaching a neutrino luminosity
of about $10^{12}$ solar luminosity ($L_{\odot}$), while the photon luminosity
is only $10^{5}L_{\odot}$ at this stage (Odrzywolek et al., 2004).

When the neutrino-cooled stage begins, massive stars typically222Some light
massive stars can proceed with oxygen-neon-magnesium burning (Giunti & Kim,
2007). proceed with nuclear fusion of helium (He), carbon (C), oxygen (O),
neon (Ne) and silicon (Si). The burning of these elements starts from the core
and then propagates toward the edges of the star in concentric shells (Woosley
et al., 1978) due to differing densities and temperatures within the stellar
volume. Closer to the onset of the gravitational collapse, the final structure
of the neutrino-cooled star is an iron core surrounded by shells containing
the products of the sequence of elemental nuclear burning phases. At this
point, neutrino-cooled stars are commonly called pre-supernova stars.

Although many processes contribute to the neutrino emission from pre-supernova
stars, at the very high temperatures at this stage the electron-positron
annihilation process generating thermal neutrinos (Eq. 1) is the star’s
dominant form of cooling:

$e^{+}+e^{-}\rightarrow\nu_{x}+\bar{\nu}_{x},$ (1)

where $x=e,\mu,\tau$.

The Si-burning phase, which is expected to last for just a few days at
temperatures of approximately $3\times 10^{9}$ K (Woosley et al., 1978;
Odrzywolek et al., 2004), creates an iron core whose inability to generate
energy via fusion into still heavier elements can then initiate the CCSN. The
electron anti-neutrinos emitted at the Si-burning stage, which correspond the
1/3 of the anti-neutrino flux (Odrzywolek et al., 2011), can exceed the energy
threshold of IBD reactions, making their detection possible in SK-Gd. Figure 1
shows the expected number of IBD interactions in SK for different Betelgeuse-
like pre-supernova models during the final 10 hours prior to core collapse for
15 M⊙ stars located 150 parsecs away from Earth.

Figure 1: Number of pre-supernova IBD interactions in the 22.5 kt SK FV
integrated over the last 10 hours prior to the CCSN as a function of the
$E_{\bar{\nu}_{e}}$. The Betelgeuse-like models consider stars with initial
masses of 15 M⊙ located 150 parsecs away from Earth, for both normal neutrino
mass ordering (NO) and inverted neutrino mass ordering (IO).

Pre-supernova $\bar{\nu}_{e}$s have never been observed before; their
detection would not only provide unique, unperturbed information detailing
otherwise hidden stellar interiors, but also contribute evidence regarding
which neutrino mass hierarchy is the correct one (Guo et al., 2019; Kato et
al., 2020). Being able to detect them also opens the possibility of creating a
pre-supernova alarm for SK, potentially delivering alerts hours before the
arrival of any other CCSN signals since the emission of pre-supernova
neutrinos takes place over a very long timescale compared to the subsequent
supernova burst neutrinos upon which most SN alarms are based.

###  3.1 Pre-Supernova Models

In addition to pair annihilation $e^{+}e^{-}\longrightarrow\bar{\nu}\nu$ for
the cooling of massive stars, beta processes are expected to also contribute
significantly at the pre-supernova stage, with an average more energetic
$\bar{\nu}_{e}$ flux (Patton et al., 2017). Due to the large interaction cross
section for IBD at both the typical pair annihilation and beta process
energies, only electron anti-neutrinos from the silicon burning phase are
expected to be detected in SK (Odrzywolek et al., 2004).

Following previous analyses (Simpson et al., 2019), in order to perform
estimations of the expected signal from pre-supernova neutrinos in SK, two
models for the thermodynamics of stellar evolution were used: (Odrzywolek &
Heger, 2010) and (Patton et al., 2017). Both models provide online data sets
for the calculation of anti-neutrino emission during the pre-supernova stage.
The first, earlier model from (Odrzywolek & Heger, 2010) assumes that the
entire neutrino flux comes from pair annihilation. For the nuclear isotopic
composition of the star, this model assumes a nuclear statistical equilibrium
(NSE), which is a treatment only dependent on the temperature, density and
electron fraction, making a flux estimate by post-processing an already
existing stellar model. The second model (Patton et al., 2017) includes a more
complete evaluation of the neutrino flux from the pre-supernova stars,
considering the contribution of individual isotopes to the stellar evolution,
which affects the neutrino emission rate from weak nuclear processes. The
model also includes contributions not only from pair annihilation, but also
from other thermal and nuclear processes such as plasmon decay, photoneutrino
process, $\beta$-decay, and electron capture.

For signal estimations, the flavor oscillations of electron anti-neutrinos as
they travel through the stellar volumes were taken into account. The
description of oscillations in matter is called the Mikheyev-Smirnov-
Wolfenstein (MSW) effect (Smirnov, 2003). The ratio by which the flux of
electron anti-neutrinos is changed depends on the electron number density of
the star and the neutrino mass ordering (as yet unknown), which changes the
mixing parameters. Different transition probabilities are assumed for normal
and inverted neutrino mass ordering to account for this change in the ratio of
electron flavor neutrinos due to the dense stellar medium in addition to the
effects of oscillations in vacuum.

##  4 Analysis Strategy

The detection of low energy electron anti-neutrinos from pre-supernova stars
in SK is mainly through the IBD interaction, where the products are a prompt
positron and a delayed neutron. Searches for IBD events consider the
coincidence of the products of the interaction.

Previous analysis (Simpson et al., 2019) of the sensitivity of SK to pre-
supernova stars considered the detection of two different detection channels;
in addition to the search for prompt and delayed parts, the search for just
the delayed neutrons individually was also used to calculate the expected
sensitivity. For this analysis, however, only coincidence events are
considered since, due to the higher rate of single neutron events, the further
development of an online alert system would be negatively affected by a
significant increase of the processing time. For this reason, techniques were
developed to optimize the online selection of only coincidence events, while
the single neutron events will be utilized in a planned future improvement of
the alert system to post-check data in case of potential alerts.

###  4.1 Event Simulation

In order to simulate the IBD events, and following what was done in (Simpson
et al., 2019), two different $\gamma$-ray emission models from neutron capture
on Gd are used: the generic liquid scintillator simulator GLG4sim (GLG4SIM,
2006), and a second model created by a group at Okayama University using data
from the Accurate Neutron-Nucleus Reaction Measurement Instrument (ANNRI)
spectrometer at the Japan Proton Accelerator Research Complex (J-PARC). The
ANNRI model was based on measurements of $\gamma$-ray properties with an array
of germanium detectors at the facility (Ou et al., 2014). The GLG4sim model
uses GEANT4 (Agostinelli et al., 2003) support for neutron capture on Gd, but
also includes spectral information regarding high energy $\gamma$-rays, which
the original GEANT4 does not consider (GLG4SIM, 2006). The ANNRI model uses
GEANT4 Monte Carlo simulation and the spectrometer data to describe the
$\gamma$-ray spectrum from neutron captures on Gd (Hagiwara et al., 2019). The
models assume an isotropic distribution for the angles of $\gamma$-ray
emission. Both are included in this analysis to provide systematic
uncertainties for the pre-supernova neutrino sensitivity results in SK.

Figure 2: Selection efficiency versus positron true energy for SK with 0.01%
Gd for the two $\gamma$-ray models used for analysis GLG4sim (GLG4SIM, 2006)
and ANNRI (Ou et al., 2014).

To reproduce IBD coincidence events, simulation of prompt positrons and
delayed neutrons were performed and put in coincidence in time and distance to
each other. The SK Monte Carlo software was employed to simulate positron
events and $\gamma$-ray events from the neutron captured on Gd, using the
GLG4sim and ANNRI models. These coincidence events were simulated in different
positions of the inner detector, with positron energies ranging between 0.8
MeV and 7.0 MeV. Figure 2 shows the signal efficiency as a function of
positron true energy for the simulations using both $\gamma$-ray models for
the event selection that will be described in Sections 4.3 and 4.4.

###  4.2 Backgrounds

The sensitivity to pre-supernova neutrinos at SK with 0.01% Gd was evaluated
using Monte Carlo and SK data prior to the first Gd loading to calculate the
contributions from different background sources, which later were compared to
SK-Gd data. The main backgrounds are:

  * •

Reactor and geo electron anti-neutrinos in the same energy region as pre-
supernova neutrinos. These are true, irreducible physics backgrounds.

  * •

Trace amounts of radioactive isotopes mixed in along with the Gd. After being
distributed in the detector these radioactive impurities could contribute to
backgrounds from $(\alpha,n)$ and spontaneous fission processes.

  * •

Accidental coincidences, which are intrinsic detector backgrounds due to
radioactive decays from the detector materials, PMT dark noise, and
uncorrelated events that randomly occur close in time and distance. These
backgrounds were evaluated by processing around 7000 hours of data from
different SK data periods.

  * •

Spallation, due to cosmic rays muons. Most of these low energy decays of
unstable nuclei can be eliminated as they are correlated in time and space
with energetic muons.

The main background sources for pre-supernova neutrinos are the reactor
neutrinos, primarily a result of the activity of Japanese nuclear power
reactors, and geoneutrinos, which come from the decay of natural radioisotopes
inside the Earth. These are irreducible backgrounds, in the same energy range
as pre-supernova neutrinos. The web page geoneutrinos.org has an application
to evaluate reactor and geoneutrino fluxes, giving the rate and energy
spectrum of anti-neutrino interactions at any location on the planet (Dye &
Barna, 2021). The application uses the IAEA’s database Power Reactor
Information System (PRIS) (IAEA, 1989) to account for each reactor’s activity
during specific periods. To give the best possible estimations of the current
reactor and geoneutrino backgrounds, when applying this tool to the pre-
supernova alarm the most up-to-date information from geoneutrinos.org is used
and further corrected with the most recently updated information regarding
reactor activity in Japan.

Backgrounds due to radioactive contamination of the Gd loading come mainly
from 18O($\alpha$,n)21Ne∗ and 17O($\alpha$,n)20Ne∗ processes, mainly a
consequence of contamination with 235U series isotopes that are $\alpha$
emitters. The decay of neon isotopes results in pairs of neutrons and the
signal of one of the neutrons can be mistaken for a positron, thereby becoming
a background for coincidence IBD events. Since the spontaneous fission of 238U
also produces one or more neutrons per fission, this also can contribute to
the overall background level.

Muons from cosmic-rays create unstable daughter nuclei through spallation,
which can in turn produce $\beta$-delayed neutrons (neutrons associated with
the beta decay of the fission products). In particular, $\beta$-ray energy for
the decay of nitrogen-17 is in the energy range of interest for this analysis.

Initially, all of the described backgrounds were evaluated with Monte Carlo,
apart from the accidental coincidences background which was calculated from
data. When SK-Gd data became available, these backgrounds were more rigorously
quantified after processing 2000 hours of data. After the event selection,
described in Section 4.4, the remaining background rate is taken into account
to calculate the sensitivity to pre-supernova neutrinos in SK.

###  4.3 Trigger Considerations

The pre-supernova alert system is integrated into an independent trigger in SK
called the Wide-band Intelligent Trigger (WIT) (Carminati, 2015; Elnimr,
2017). WIT is a system designed to extend the sensitivity of SK to lower
energy events using parallel computing to reconstruct vertices in real time,
discarding events that are not well reconstructed or very close to the walls
of the detector. The WIT system consists of more than 400 hyper-threaded cores
spread over ten online computers that work in parallel with each other. The
system will be significantly upgraded during 2022 with a batch of new
computers containing hundreds of additional cores.

The WIT system receives raw data blocks containing about 23 milliseconds of
data each from data acquisition machines; these blocks are then distributed to
the ten online computers to search for signals (11 PMT hits within 230
nanoseconds) above expected dark noise levels (12 hits) inside these blocks.

The hits passing this procedure proceed to another filter called STORE
(Software Triggered Online Reconstruction of Events). STORE looks for PMT hits
originating from a single vertex by considering the Cherenkov emission of low
energy electrons a point source, a reasonable assumption since these electrons
can only travel a few centimeters in water (Carminati, 2015), which is much
smaller than SK’s vertex resolution. STORE selects the largest set of hits
that are consistent with being from a point source. Another trigger condition
applied requires that for any two PMT hits the time difference between them
must be less than or equal to the time required to travel between them at the
speed of light in water. From these selected hits, STORE gathers four-hit
combinations, using their arrival times to calculate vertex position, in which
the time residuals ((time of the PMT hit) - (time of flight) - (time of the
test vertex)) of the four hits must be zero.

The selected hits from STORE go through a fast vertex reconstruction called
ClusFit, which eliminates isolated hits to reduce effects of reflected and
scattered light and dark noise (Carminati, 2015). The fast online vertex
reconstruction in WIT is concluded by applying BONSAI (Branch Optimization
Navigating Successive Annealing Iterations) (Smy, 2007) to the selected events
from Clusfit that are at least 1.5 m away from any PMT.

BONSAI uses events within 500 ns before and 1000 ns after the trigger time,
performing a maximum likelihood fit to all arrival times of PMT hits inside
this window, testing for each one a vertex hypothesis and choosing the ones
with maximized likelihood as the reconstructed vertices. BONSAI saves the 1500
ns events that are inside the fiducial volume (2 m away from the inner PMT
wall) and that have at least 10 hits with time residuals within $-6$ ns and
$+12$ ns (Locke et al., 2021).

The reconstructed events are sent to WIT’s organizer machine, which is a
computer responsible for sorting the events in time, gathering about 90
seconds worth of data into one file. The sorted events are then transferred to
the offline computers for further processing by the various offline SK physics
analyses.

###  4.4 Event Selection

To select the IBD pre-supernova events, multivariate methods are used, in
particular Boosted Decision Tree (BDT) (Coadou, 2013), which is applied at two
different levels of the data processing for reasons of both speed and
efficiency. While one of the BDTs is used as the final selection of pre-
supernova events, the other BDT, called BDTonline, is applied to search for
IBD candidate events in the WIT data.

The initial search for IBD pair candidates is performed using the number of
hit PMTs and the reconstructed position and time of events. This information
is available online when data is being saved by the WIT trigger, looping over
all events passing the fast online reconstruction. For each prompt event
candidate, a delayed candidate event is searched in a time window from -17
$\mu s$ to 290 $\mu s$ relative to the prompt candidate (each hardware trigger
is 17 $\mu s$ long). If the delayed candidate has a minimum number of hit
PMTs, the time between the prompt and delayed candidates, dT, as well as the
distance between their reconstructed positions, dR, are calculated.

As described in Section 2, IBD events are very well characterized by the
spatial and timing coincidences between the prompt and delayed signals,
represented by the variables dR (distance) and dT (time). When comparing
accidental coincidences with signal, these variables have very different
distributions. However, a selection using only this information is not enough
to sufficiently reduce backgrounds. For that reason BDTs were implemented in
the analysis.

Figure 3: Signal-background separation for the two Boosted Decision Tree
classifiers used for (a) pre-selection (BDTonline) and (b) final selection.

BDTonline, applied during the initial search for signal events, was trained
based only on the variables available online from fast WIT online vertex
reconstruction; BDTonline is used at this early realtime processing stage as a
pre-selection of potential IBD candidates. The final selection of events is
done by applying cuts in dR, dT, and to a score resulting from another BDT
classifier, which is based on angular distribution of hits, reconstructed
energy and quality, and distance from events to the detector wall. Figure 3
shows the signal and background separation for both BDTs. Both BDTs are
trained using random subsets of SK-Gd data as background and a portion (about
30%) of the generated IBD coincidence events using the GLG4sim model, as
described in Section 4.1, as signal.

Using two BDTs in the selection process is justified by the need for having
fast processing time in the alert system, since it is an online system that
constantly and uninterruptedly processes many data sets, running statistical
evaluations to make alarm decisions and send alerts. For this reason, the
system needs to process data as quickly as possible, but without losing
detection efficiency. BDTonline improved the speed of the alert system by
filtering pair candidates more efficiently, reducing the number of background
events being carried through the full data reduction. The combination of the
two BDTs ultimately resulted in better sensitivity to pre-supernova neutrinos
than using only one of them.

Analyzing 2000 hours of SK-Gd data, optimizations to the selection were
performed and the backgrounds’ contributions were re-evaluated in order to
quantify the contributions from the different sources and better predict the
sensitivities of the current and future SK-Gd loading phases. These results
are described in the next section.

##  5 Sensitivity to Pre-supernova Neutrinos

Previous sensitivity results for detection of pre-supernova neutrinos in SK
with 0.1% Gd were given in (Simpson et al., 2019). However, the first – and
current, as of this writing – phase of SK-Gd has a concentration of 0.01% Gd,
and the development of the pre-supernova alert system required a dedicated
response model for the current phase. In this section, the estimated
sensitivity to pre-supernova neutrinos is presented. General details about the
alert system, its expected early warning times, and its coverage of our galaxy
are covered in Section 6.

Figure 4: Expected warning times for Betelgeuse-like models evaluated as a
function of the chosen time window to perform statistical evaluations in
hourly steps. The estimations consider the neutrino flux from the last 70
hours before core-collapse for stars with 15 M$\odot$ at 150 parsecs and 25
M$\odot$ at 220 parsecs following the model (Odrzywolek & Heger, 2010), and 15
M$\odot$ stars at 150 parsecs following (Patton et al., 2017). Normal neutrino
mass hierarchy is assumed. Figure 5: Comparison of SK-Gd data and simulation
for coincidence distribution variables (a) distance dR, (b) time dT, and (c)
prompt reconstructed energy. Dark colors represent the variables before the
final selection and light colors after the selection. Histograms are
normalised to area = 1.

Some important statistical parameters such as signal and background time-
windows, hypothesis tests, and false positive rates need to be taken into
account to estimate the sensitivities. The statistical evaluation that is
performed by the alert system is also used to provide the early warnings and
estimate the range of detection of pre-supernova stars. These evaluations are
performed as Poissonian counting experiments (Lista, 2015). For counting
experiments, the p-value is the probability of counting a total number of
events greater than or equal to the expected number in which only background
and no signal is present. One way to calculate the p-value of the total number
of selected events inside a pre-defined time window $N_{events}$, for an
expected number of background events $N_{BG}$ is:

$p=P(N_{events}\geq N_{BG})=\sum_{n=N_{events}}^{\infty}Pois(n;N_{BG})$ (2)

where $Pois(n;N_{BG})$ is the Poisson distribution of the variable $n$ with
average $N_{BG}$.

$\alpha$-Ori (Betelgeuse in the constellation Orion) is currently the best
candidate for detecting pre-supernova neutrinos. Cuts and statistical
parameters were optimized to give the longest early warnings possible for
Betelgeuse. Even in the best current estimations, its mass and distance from
the Earth remain somewhat uncertain but are highly correlated; consequently,
results are given in combinations to match what is commonly found in the
literature and the available pre-supernova models: 150 pc for 15 M$\odot$ and
220 pc for 25 M$\odot$ (Dolan et al., 2016; Harper et al., 2017; Joyce et al.,
2020). The signal window chosen to perform statistical evaluations was based
on which choice would maximize the warning time for Betelgeuse-like models –
the earliest alerts would be sent for a potential CCSN of Betelgeuse. Figure 4
shows the warning time for Betelgeuse-like models in the last 70 hours before
CCSN as a function of the signal time window used for statistical evaluations.
A signal window in the interval from 7 hours to 13 hours would be one in which
all Betelgeuse-like models have their longest early warnings. An window near
the lower end of this range, 8 hours, but comfortably removed from the steep
falloff in warning times seen with shorter windows, was chosen to maximize the
early warning time while simultaneously minimizing potential negative impacts
of the pre-supernova alarm inducing long interruptions in the usual data
acquisition or planned calibration work in the SK detector.

###  5.1 Results for SK’s First Gadolinium Loading

The following results are dependent on the neutrino mass ordering, ZAMS mass
of the star, distance to the star, and star evolution model. The models for
neutron capture on Gd being used are the $\gamma$-ray models GLG4sim and
ANNRI, employed to give systematic uncertainties to the results.

With the implementation of BDTonline, sensitivity estimations were improved
and faster data processing was achieved, as fewer events had to be carried
through the reduction after the initial search for pair candidates. Other
optimization cuts to the variables used for final selection were also
performed in order to maximize potential early warnings. The optimized cuts
used for final selection are: coincidence distance $dR<300$ cm, coincidence
time $dT<150$ $\mu$s, and $BDT\ score>-0.10$, resulting in an irreducible
background rate of approximately $0.1$ events/hour333Background levels are
periodically updated for the alert system.. Figure 5 shows some key
distributions before and after the selection for the full samples of 2000
hours of SK-Gd data and IBD generated events using the GLG4sim model. The
selection with BDTs also removed any visible bursts in data, which were
presumably from spallation.

Betelgeuse is the tenth brightest star in the sky. It is only about 8 million
years old, but had a very quick evolution and is believed to now be close to
becoming a CCSN. Table 1 shows the expected number of IBD events for
Betelgeuse in SK with 0.01% Gd, following estimations to its mass and distance
in the literature. Since the $\gamma$-ray emission models, GLG4sim and ANNRI,
yield very similar results, the model GLG4sim was chosen as the baseline model
for all subsequent analyses.

Table 1: Number of expected IBD events for Betelgeuse in Super-Kamiokande with 0.01% Gd in the final eight hours before becoming a core-collapse supernova. Results are shown for two different $\gamma$-ray emission models. Betelgeuse-like | Neutrino Mass Hierarchy |
---|---|---
Model | Normal Ordering | Inverted Ordering |
15 M$\odot$, 150 parsecs |  |  |
Odrzywolek | 30.9 – 31.5 | 8.8 – 8.9 |
Patton | 41.9 – 42.7 | 12.5 – 12.7 |
25 M$\odot$, 220 parsecs |  |  |
Odrzywolek | 25.0 – 25.5 | 7.1 – 7.2 |
Figure 6: Integrated number of IBD events in Super-Kamiokande with 0.01% Gd as
a massive star (d = 200 pc) approaches core-collapse. Solid lines show normal
neutrino mass hierarchy and dashed lines show inverted neutrino mass
hierarchy. The considered fluxes are evaluated for stars with 15 M$\odot$ and
25 M$\odot$ following the model (Odrzywolek & Heger, 2010), and also
alternatively for 15 M$\odot$ stars following (Patton et al., 2017).

Figure 6 shows the evolution of the expected number of IBD events in SK-Gd
with 0.01% Gd after cuts for different pre-supernova models for stars at 200
parsecs for both normal and inverted neutrino mass hierarchies. As expected,
the event rates increase significantly closer to the explosion. Figure 7 shows
the evolution of detection significance of these pre-supernova models at 200
parsecs in the hours leading up to the CCSN.

Figure 7: Evolution of the significance level in Super-Kamiokande with 0.01%
Gd over the last 50 hours before the core-collapse for massive stars at d =
200 pc. Solid lines show normal neutrino mass hierarchy and dashed lines show
inverted neutrino mass hierarchy. The considered fluxes are evaluated for
stars with 15 M$\odot$ and 25 M$\odot$ following the model (Odrzywolek &
Heger, 2010), and also alternatively for 15 M$\odot$ stars following (Patton
et al., 2017).

Figure 8 shows the number of IBD events expected in SK-Gd with 0.01% Gd as a
function of the distance to the star integrated over the last eight hours
preceding the CCSN. Background rates are also shown. These results indicate
that SK with 0.01% Gd can observe pre-supernova neutrinos with a 3$\sigma$
detection significance for stars up to 600 parsecs away from Earth.

Figure 8: Expected number of IBD events in Super-Kamiokande with 0.01% Gd as a
function of distance. Solid lines show normal neutrino mass hierarchy and
dashed lines show inverted neutrino mass hierarchy. The considered fluxes are
integrated over eight hours preceding the core-collapse, for stars with 15
M$\odot$ and 25 M$\odot$ following the model (Odrzywolek & Heger, 2010), and
also alternatively for 15 M$\odot$ stars following (Patton et al., 2017).

These results demonstrate the possibility of detecting pre-supernova neutrinos
even in the first phase of SK-Gd, and also suggest the viability of creating a
pre-supernova alarm based on their detection. Discussions about the
development of such a pre-supernova alert system will be presented in Section
6. Subsequent phases of SK-Gd will have greater Gd concentrations, and
predictions for their pre-supernova sensitivities will be shown next.

###  5.2 Future SK Gadolinium Loading Phases

After characterizing backgrounds with the current SK-Gd data, evaluations of
sensitivities for future Gd loading concentrations were performed. The next
proposed phase is for a concentration of 0.03% Gd, while the final phase will
have a concentration of 0.10% Gd. Since November 2020, the 200-ton EGADS
detector has been loaded with 0.03% Gd and taking data; all has gone well,
verifying the feasibility of the next SK-Gd phase planned to begin in
mid-2022. With higher concentrations of Gd in the SK water, neutrons will be
observed with increasing efficiency, which will enhance the experiment’s pre-
supernova neutrino detection capabilities.

Figure 9: Expected number of IBD events in Super-Kamiokande with (a) 0.03% Gd
and (b) 0.10% Gd as a function of distance. Solid lines show normal neutrino
mass hierarchy and dashed lines show inverted neutrino mass hierarchy. Results
are shown considering two reactor scenarios: with low reactor flux (all
Japanese reactors turned off), and high reactor flux (double the current
contribution). The considered fluxes are integrated over eight hours preceding
the core-collapse, for stars with 15 M$\odot$ and 25 M$\odot$ following the
model (Odrzywolek & Heger, 2010), and also alternatively for 15 M$\odot$ stars
following (Patton et al., 2017).

Figure 9 shows the number of IBD events expected in SK integrated over the
last eight hours preceding a CCSN as a function of the distance to the star
for the future phases of SK-Gd. Estimated increases in the background rates
are also shown.

Backgrounds to the pre-supernova IBD signal will naturally increase as the Gd
concentration is raised, since doing so will also mean higher efficiencies for
detecting the IBD reactions arising from reactor and geo neutrinos, which are
the main physics backgrounds to pre-supernova neutrinos. In addition, further
dissolving of Gd salts in the detector will mean greater overall radioactive
contamination, increasing the rate of backgrounds from $(\alpha,n)$ and
spontaneous fission processes. Due to the uncertain future of nuclear power in
Japan, backgrounds are considered for two scenarios: with low reactor fluxes,
in which all Japanese reactors are off, and with high reactor fluxes, meaning
double the current reactor contributions. These preliminary results show that,
in optimistic scenarios, for SK-Gd with 0.03% the pre-supernova neutrinos can
be detected from stars up to about 700 parsecs away, and for 0.10 % Gd the
detection radius will be over 700 parsecs. As will be discussed later, future
Gd loading phases should also enable earlier warnings from the alert system.

Other current and near-future neutrino experiments such as SNO+ (Albanese et
al., 2021) and JUNO (JUNO Collaboration, 2022) expect to have the ability to
detect pre-supernova neutrino signals, while future dark matter direct
detection experiments could detect all flavors of pre-supernova neutrinos via
coherent neutrino-nucleus scattering (Raj et al., 2020). The Kamioka Liquid
Scintillator Antineutrino Detector (KamLAND) has an active pre-supernova alarm
and comparisons with SK-Gd are shown next.

###  5.3 Comparison to KamLAND

The KamLAND experiment – also located in the Kamioka mine – has a working pre-
supernova alert system with expected sensitivities comparable to SK-Gd.
KamLAND is a liquid scintillator detector, with lower energy thresholds than
SK and lower background rates. The pre-supernova alert system in KamLAND works
with a set background rate of between 0.071 and 0.355 events/day, depending on
the reactor activity in Japan, and integrates selected events over a 48 hour
time window every 15 minutes (Asakura et al., 2016). KamLAND can be expected
to see many fewer pre-supernova events than Super-Kamiokande, however, since
KamLAND’s fiducial volume is less than 5% that of SK.

Based on the results reported in (Asakura et al., 2016), the relative
sensitivity to pre-supernova neutrinos of the current SK-Gd phase and KamLAND
is shown in Figure 10. Figure 11 shows the same comparison for future SK-Gd
phases.

Figure 10: Expected number of IBD events in Super-Kamiokande with 0.01% Gd and
for KamLAND as a function of distance. All Japanese reactors are assumed to be
off. Solid lines show normal neutrino mass hierarchy and dashed lines show
inverted neutrino mass hierarchy. The considered fluxes are integrated over
eight hours preceding the core-collapse for SK-Gd and 48 hours for KamLAND,
for stars with 15 M$\odot$ and 25 M$\odot$ following the model (Odrzywolek &
Heger, 2010). Figure 11: Expected number of IBD events in KamLAND and in
Super-Kamiokande with (a) 0.03% Gd and (b) 0.10% Gd as a function of distance.
All Japanese reactors are assumed to be off. Solid lines show normal neutrino
mass hierarchy and dashed lines show inverted neutrino mass hierarchy. The
considered fluxes are integrated over eight hours preceding the core-collapse
for SK-Gd and 48 hours for KamLAND, for stars with 15 M$\odot$ and 25 M$\odot$
following the model (Odrzywolek & Heger, 2010).

Combining results of both detectors could lead to extended detection ranges
and earlier warnings for a joint alarm; discussions between the Collaborations
have been initiated with this goal in mind.

##  6 Online Search for Pre-Supernova Neutrino Signal

Super-Kamiokande’s pre-supernova alert system is intended to provide early
warning of an incipient nearby supernova based on the detection of pre-
supernova neutrinos. It became operational – analyzing realtime data and ready
to send out alarms of potential CCSNs – on October 22nd, 2021.

The alarm software is installed in the WIT online computer cluster, performing
its pre-supernova neutrino search right after the fast online vertex
reconstruction, as described in Section 4.3, is completed. The alarm has its
own organizing processes which utilize: 1) information from the hardware
trigger, a real-time counter used to group together acquired data in time, and
2) reconstructed time of events. This allows the alarm software to calculate
the absolute time of events and sort them in time.

To avoid false alerts, a volume cut is applied around calibration sources
deployed inside the detector. Also, when calibration work is actively being
performed in SK the system automatically ignores the data from that period by
not processing any new data until the work is finished. False alerts from a
statistical point of view will be discussed later in Section 6.2.

###  6.1 Data Flow

The alert system works with default values of background rate and signal time
window, which are parameters used for statistical evaluation. As background
levels are periodically updated, it is possible to change the input values of
these parameters in the system.

When active, the system checks all the available data from the WIT trigger,
creating a “to-do” queue, which includes data that have not been processed
yet. All data in the queue go through reduction, which starts with the search
of IBD pair candidates. This is followed by a pre-selection of the pairs,
which includes BDTonline. Then a final selection takes place using coincidence
distance dR, coincidence time dT, and a BDT based on angular distribution of
hits, reconstructed energy and quality, and distance from events to the
detector wall, as explained in Section 4.4. When the queue is empty, the
system will look again for new data to create a new ”to-do” queue.

All of the IBD pair candidates have their information saved: the absolute time
of events, coincidence time, distance between prompt and delayed parts of the
IBD event, and reconstructed vertices. This information is also used to
graphically follow trends in the time evolution of candidate events and
significance of detection, as well as the distribution of these events inside
the SK detector.

A rough estimation for the latency time of the alert system, which is the
interval between the time when an event actually occurs, is processed through
WIT, and the alert system makes an alarm decision, is summarized in Table 2
based on tests performed on the system. Since they occur sequentially, the
times in this table must be added together to arrive at the total latency,
which is currently about 11 minutes. As mentioned previously, the WIT system
is now being upgraded with new machines; this is expected to reduce the
overall latency time to well below 10 minutes.

Table 2: Estimated latency time of each step in the pre-supernova alert system. Total latency time is the sum of the the latency of each step.  Process | Estimated Time
---|---
Data Fitting (WIT system) | 4 minutes
Process Queue ($\sim 2\times 10^{6}$ events) | 5 minutes
Alarm Decision/Export Results | 2 minutes

Every time a new subset of data is processed, the system calls a function to
perform the statistical evaluation, which counts the number of events that
were selected inside the signal time window in order to make an alarm
decision. If the decision is positive, the system sends alerts regarding the
possibility of a supernova.

The Super-Kamiokande detector is monitored constantly by collaborators. The
status of the pre-supernova alert system is included in the SK monitoring via
a web page which displays realtime information from the system. In addition to
technical status-related data, the web page includes the latest results from
(and history of) the alert system’s statistical evaluations. These graphs of
the recent IBD pair candidate rate and evolution of the resulting significance
level can be used to monitor unusual fluctuations, in particular sudden
increments in IBD pair candidates and detection significance which can lead to
potential alerts.

###  6.2 Alarm Decision

The alarm decision determines whether the software should send an alert or
not. Many factors have to be taken into account, such as the current status of
the detector, background rate levels, and false positive rates.

After processing new data, the running number of selected IBD events undergoes
a statistical evaluation to determine the significance of the detection,
calculated with Poisson statistics as discussed in Section 5. When testing a
hypothesis, one possible outcome is a false positive, which is the probability
of believing a condition exists when it does not.

The choice of an acceptable false positive rate (FPR) is often related to an
experiment’s sensitivity, since requiring greater confidence in a result in
order to reduce the FPR means less chance of positively identifying more
marginal, but nevertheless real, signals. Consequently, a decrease in the
false positive rate implies an increase in the experiment’s false negative
rate (FNR), and vice versa.

In order to have a real-world basis for the pre-supernova alert system’s
chosen FPR, it was decided that the rate of core-collapse supernova explosions
in our galaxy should serve as the maximum FPR. In other words, statistically
speaking the system should not issue false alerts more frequently than Milky
Way (MW) core-collapse explosions are expected to take place. Of course, the
true rate of detectable pre-supernova explosions will be considerably lower
than this since they must be located much closer to Earth than the average MW
explosion, but the trade-off between FPR and FNR means that allowing the
occasional false positive will improve sensitivity to the true positives.

From (Adams et al., 2013) the rate of galactic core-collapse supernova
explosions is taken to be $3.2_{-2.6}^{+7.3}$ per century. This rate is
derived using all supernova events observed over the last millennium with
various corrections applied. The most important correction is due to what
fraction of the galaxy is visible to the naked eye, since all recorded
explosions inside the MW were observed before the invention of the telescope.

To introduce this factor, inside the signal window of eight hours chosen to
perform statistical evaluations a cut-off is applied to the p-value:
$\frac{3.2}{century}$ per 8 hours $\sim 7.4\times 10^{-5}$, requiring the
alarm to send alerts in case the number of IBD events inside the signal window
are enough to give significance levels above $\sim 4\sigma$. However, if the
significance level exceeds 3$\sigma$ then a preliminary alert is sent to SK
experts so they may begin monitoring the situation as it develops.

Figure 12: Expected warning time as function of distance for a 3$\sigma$
detection by the pre-supernova alert system for Super-Kamiokande with 0.01%
Gd. Solid lines show normal neutrino mass hierarchy and dashed lines show
inverted neutrino mass hierarchy. The considered fluxes are evaluated for
stars with 15 M$\odot$ and 25 M$\odot$ following the model (Odrzywolek &
Heger, 2010), and also alternatively for 15 M$\odot$ stars following (Patton
et al., 2017). Table 3: List of likely pre-supernova candidates with estimated
masses and distances.

Catalog Name | Common Name | Constellation | Distance | Mass
---|---|---|---|---
|  |  | (kpc) | (M$\odot$)
HD 116658 | Spica/$\alpha$ Virginis | Virgo | $0.0766\pm 0.0041{}^{(a)}$ | ${11.43}^{+1.15}_{-1.15}{}^{(b)}$
HD 149757 |  $\zeta$ Ophiuchi | Ophiuchus | $0.107\pm 0.004{}^{(c)}$ | ${13}^{+10}_{-6}{}^{(d)}$
HD 129056 |  $\alpha$ Lupi | Lupus | $0.143\pm 0.003{}^{(a)}$ | ${10.1}^{+1.0}_{-1.0}{}^{(a)}$
HD 78647 |  $\lambda$ Velorum | Vela | $0.167\pm 0.003{}^{(a)}$ | ${7.0}^{+1.5}_{-1.0}{}^{(a)}$
HD 148478 | Antares/$\alpha$ Scorpii | Scorpius | $0.169\pm 0.030{}^{(a)}$ | ${11.0-14.3}{}^{(a)}$
HD 39801 | Betelgeuse/$\alpha$ Orionis | Orion | $0.168^{+0.027}_{-0.015}{}^{(e)}$ | ${16.5}-{19}{}^{(e)}$
HD 206778 |  $\epsilon$ Pegasi | Pegasus | $0.211\pm 0.006{}^{(a)}$ | ${11.7}^{+0.8}_{-0.8}{}^{(a)}$
HD 89388 | q Car/V337 Car | Carina | $0.230\pm 0.020{}^{(a)}$ | ${9.0}\pm 1.6{}^{(f)}$
HD 34085 | Rigel/$\beta$ Orion | Orion | $0.237^{+0.057}_{-0.038}{}^{(g)}$ | ${21.0}^{+3.0}_{-3.0}{}^{(a)}$
HD 210745 |  $\zeta$ Cephei | Cepheus | $0.256\pm 0.006{}^{(a)}$ | ${10.1}^{+0.1}_{-0.1}{}^{(a)}$
HD 200905 |  $xi$ Cygni | Cygnus | $0.278\pm 0.029{}^{(a)}$ | $8.0{}^{(a)}$
HD 47839 | S Monocerotis A | Monoceros | $0.282\pm 0.040{}^{(a)}$ | $29.1{}^{(a)}$
HD 47839 | S Monocerotis B | Monoceros | $0.282\pm 0.040{}^{(a)}$ | $21.3{}^{(a)}$
HD 93070 | w Car/V520 Car | Carina | $0.294\pm 0.023{}^{(a)}$ | ${7.9}^{+0.1}_{-0.1}{}^{(a)}$
HD 68553 | NS Puppis | Puppis | $0.321\pm 0.032{}^{(a)}$ | $9.7{}^{(a)}$
HD 36389 | CE Tauri/119 Tauri | Taurus | $0.326\pm 0.070{}^{(a)}$ | ${14.37}^{+2.00}_{-2.77}{}^{(a)}$
HD 68273 |  $\gamma$2 Velorum | Vela | $0.379\pm 0.004{}^{(h)}$ | ${9.0}^{+0.6}_{-0.6}{}^{(a)}$
HD 50877 | ø1 Canis Majoris | Canis Major | $0.394\pm 0.052{}^{(a)}$ | ${7.83}^{+2.0}_{-2.0}{}^{(a)}$
HD 52877 |  $\sigma$ Canis Majoris | Canis Major | $0.513\pm 0.108{}^{(a)}$ | ${12.3}^{+0.1}_{-0.1}{}^{(a)}$
HD 208816 | VV Cephei | Cepheus | $0.599\pm 0.083{}^{(a)}$ | ${10.6}^{+1.0}_{-1.0}{}^{(a)}$
HD 203338 | V381 Cephei | Cepheus | $0.631\pm 0.086{}^{(a)}$ | $12.0{}^{(a)}$
HD 17958 | HR 861 | Cassiopeia | $0.639\pm 0.039{}^{(a)}$ | ${9.2}^{+0.5}_{-0.5}{}^{(a)}$
HD 80108 | HR 3692 | Vela | $0.650\pm 0.061{}^{(a)}$ | ${12.1}^{+0.2}_{-0.2}{}^{(a)}$

Note. —

a: (Mukhopadhyay et al., 2020) (and references therein)

b: (Nieva, M.-F. & Przybilla, N., 2012)

c: (Renzo & Götberg, 2021)

d: (Marcolino et al., 2009)

e: (Joyce et al., 2020)

f: (Kallinger, T. et al., 2019)

g: (Chesneau, O. et al., 2014)

h: (Rate et al., 2020)

###  6.3 Alert System Sensitivity

The expected warning time as function of distance for a 3$\sigma$ detection by
SK-Gd with 0.01% Gd is shown in Figure 12. In the most optimistic scenario, in
which Betelgeuse’s true mass is 15 M$\odot$ at a distance of 150 parsecs and
with normal neutrino mass hierarchy, following the model from (Patton et al.,
2017) the alert would be about 9 hours before the core-collapse supernova.
Table 3 lists a number of nearby stars at different distances which could
potentially provide observable pre-supernova neutrino signals.

####  6.3.1 Sensitivity for Future SK-Gd Phases

The expected early warning times for future SK-Gd phases were also evaluated
after characterizing backgrounds using SK-Gd data. Although – as previously
discussed – the background rates will be higher during the future phases, the
expected sensitivity will also increase. Figure 13 shows the expected warning
times at which the system would send alerts as function of distance for a
3$\sigma$ detection for SK with 0.03% and 0.10% Gd.

Figure 13: Expected warning time as function of distance for a 3$\sigma$
detection by the pre-supernova alert system for Super-Kamiokande with (a)
0.03% Gd and (b) 0.10% Gd. The edges of the bands show two background
scenarios considering low reactor flux (All Japanese reactors off) and high
reactor flux (double the current contribution). Solid lines show normal
neutrino mass hierarchy and dashed lines show inverted neutrino mass
hierarchy. The considered fluxes are evaluated for stars with 15 M$\odot$ and
25 M$\odot$ following the model (Odrzywolek & Heger, 2010), and also
alternatively for 15 M$\odot$ stars following (Patton et al., 2017).

During future phases of SK-Gd and for the same optimistic scenario discussed
above, Betelgeuse would have early warning alerts about 10 to 12 hours in
advance of its end as a core-collapse supernova.

##  7 Conclusion

Super-Kamiokande is a neutrino observatory in operation since 1996 that has
recently entered its SK-Gd phase; gadolinium was loaded into the detector in
2020, achieving a concentration of 0.01% Gd. This phase is characterized by
increased sensitivity to thermal neutrons due to the enormous capture cross
section of gadolinium, which then emits easily detectable $\gamma$-ray
cascades of about 8 MeV total energy. In turn, the highly visible neutron
captures made possible by Gd loading greatly enhance the detector’s
sensitivity to low energy electron anti-neutrinos $\bar{\nu}_{e}$, as their
dominant interaction channel is via inverse beta decay which yields a positron
and neutron in the final state. Consequently, the Gd-loaded Super-Kamiokande
now has the potential to detect as-yet-unobserved neutrinos from different
astronomical sources such as the diffuse supernova neutrino background and
pre-supernova stars.

Stars with greater than 8 solar masses and a growing iron core are in the
final stages of fusion shortly before core-collapse; these are commonly known
as pre-supernova stars. Their main cooling mechanism is the production of low
energy neutrinos and anti-neutrinos from thermal and weak nuclear processes.
Some of these neutrinos have enough energy to exceed the inverse beta decay
energy threshold, and – if the star is close enough – can therefore be
detected at SK-Gd. Pre-supernova models from (Odrzywolek & Heger, 2010) and
(Patton et al., 2017) were used to evaluate the sensitivity of Super-
Kamiokande with 0.01% gadolinium to pre-supernova neutrinos. For this
gadolinium concentration, estimations showed that pre-supernova stars can be
observed in SK-Gd up to an optimistic distance of 600 parsecs away from Earth.

The emission of neutrinos from pre-supernova stars occurs for hours before the
core collapses, which could provide an early warning for potential supernova
events. A pre-supernova alert system was developed for Super-Kamiokande and it
has been active since October 22nd, 2021. In the case of $\alpha$-Ori
(Betelgeuse) with optimistic parameters, Super-Kamiokande would produce alerts
up to 9 hours before the core-collapse supernova. Nearly 20 other nearby stars
could have an early warning of their coming explosion via the detection of
their pre-supernova neutrinos. Estimations showed that for future phases of
Super-Kamiokande with increased concentrations of gadolinium, detection ranges
and early alert times of the pre-supernova alarm would be extended.

## Acknowledgements

We gratefully acknowledge the cooperation of the Kamioka Mining and Smelting
Company. The Super‐Kamiokande experiment has been built and operated from
funding by the Japanese Ministry of Education, Culture, Sports, Science and
Technology; 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 NRF‐2009‐0083526 (KNRC) funded by the Ministry of Science,
ICT, and Future Planning and the Ministry of Education (2018R1D1A1B07049158,
2021R1I1A1A01059559); the Japan Society for the Promotion of Science; the
National Natural Science Foundation of China under Grants No.11620101004; the
Spanish Ministry of Science, Universities and Innovation (grant
PGC2018-099388-B-I00); the Natural Sciences and Engineering Research Council
(NSERC) of Canada; the Scinet and Westgrid consortia of Compute Canada; the
National Science Centre (UMO-2018/30/E/ST2/00441) and the Ministry of
Education and Science (DIR/WK/2017/05), Poland; the Science and Technology
Facilities Council (STFC) and GridPPP, UK; the European Union’s Horizon 2020
Research and Innovation Programme under the Marie Sklodowska-Curie grant
agreement no.754496, H2020-MSCA-RISE-2018 JENNIFER2 grant agreement no.822070,
and H2020-MSCA-RISE-2019 SK2HK grant agreement no. 872549. For the purposes of
open access, the authors have applied a creative commons attribution (CC BY)
licence to any author accepted manuscript version arising.

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