Università degli Studi di Napoli Federico II
DOTTORATO DI RICERCA IN
FISICA
Ciclo XXXIII
Coordinatore: prof. Salvatore Capozziello

Pre-Supernova Alert System forSuper-Kamiokande withGadolinium

Settore Scientifico Disciplinare _____/______

Dottorando
Lucas NASCIMENTO MACHADO

Anni 2018/2021

Tutor
Prof.ssa Gianfranca DE ROSA
Prof. Vittorio PALLADINODeclaration of Authorship
I, Lucas Nascimento Machado, declare that this thesis titled, ‘Pre-Supernova
Alert System for Super-Kamiokande with Gadolinium’ and the work presented in
it are my own. I confirm that:⌅

This work was done wholly or mainly while in candidature for a research
degree at this University.

⌅

Where any part of this thesis has previously been submitted for a degree or
any other qualification at this University or any other institution, this has
been clearly stated.

⌅

Where I have consulted the published work of others, this is always clearly
attributed.

⌅

Where I have quoted from the work of others, the source is always given.
With the exception of such quotations, this thesis is entirely my own work.

⌅

I have acknowledged all main sources of help.

⌅

Where the thesis is based on work done by myself jointly with others, I havemade clear exactly what was done by others and what I have contributed
myself.

Signed:Date:

14/05/2021

1

UNIVERSITÀ DEGLI STUDI DI NAPOLI FEDERICO II

Abstract (English)”Ettore Pancini” Physics Department
Doctor of Philosophy
by Lucas Nascimento Machado

The current phase of the Super-Kamiokande experiment, SK-Gd, is characterized by the addition of gadolinium sulfate to the water Cherenkov detector, which
improves the detection capability of thermal neutrons. For low energy events,
the main detection channel for electron anti-neutrinos is the Inverse Beta Decayinteraction, which has, in its final state, a positron and a neutron. The neutron
thermal capture by gadolinium emits an 8 MeV gamma-ray cascade, improving theidentification of the products of this process. This improved identification reduces
the background for low energy events, allowing the analysis of neutrinos with energies below the usual Super-Kamiokande thresholds. One possible detection by
SK-Gd is the neutrinos coming from pre-Supernova stars, which correspond to
the last evolutionary state of massive stars before core-collapse Supernova. During this stage, pair annihilation and beta decay processes are the main coolingmechanisms of the massive stars, emitting high fluxes of electron anti-neutrinos.
Their detection could provide an early warning for core-collapse Supernovae. The
techniques for the development of the pre-Supernova alert system for SK-Gd arepresented in this thesis.

UNIVERSITÀ DEGLI STUDI DI NAPOLI FEDERICO II

Abstract (Italian)
Dipartimento di Fisica ”Ettore Pancini”
Dottore in Filosofia
di Lucas Nascimento Machado

L’esperimento Super-Kamiokande ha recentemente iniziato una nuova fase sperimentale, SK-Gd, caratterizzata dall’aggiunta di solfato di gadolinio al rilevatore
Cherenkov, migliorando cosı̀ la capacità di rivelazione dei neutroni termici. Per
eventi a bassa energia, il canale di rivelazione principale per gli anti-neutrini elettronici è il processo di decadimento beta inverso, che ha, nel suo stato finale, unpositrone e un neutrone. La cattura termica dei neutroni in gadolinio emette una
cascata di raggi gamma di 8 MeV, migliorando l’identificazione dei prodotti diquesto processo. Questa migliore identificazione riduce il background per eventi dibassa energia, consentendo l’analisi di neutrini con energie al di sotto dei liveli finoad oggi accessibili in Super-Kamiokande. In SK-Gd è possibilie la rivelazione di
neutrini da pre-Supernova, che si prevede siano emessi nell’ultimo stato evolutivo
di stelle di massa maggiore di otto masse solari, prima del collasso. Durante questa
fase, la produzione di coppie neutrini e antineutrini e i procesi di decadimento betasono il principale meccanismo di ra↵reddamento e ci si aspetta un flusso intenso dianti-neutrini elettronici. La rivelazione di questi anti-neutrini elettronici potrebbe
fornire un preallarme per collassi di Supernovae. In questa tesi vengono presentati
i sistemi di allerta pre-Supernova per SK-Gd.

UNIVERSITÀ DEGLI STUDI DI NAPOLI FEDERICO II

Resumo (Portuguese)
Departamento de Fsica ”Ettore Pancini”
Doutor em Filosofia
por Lucas Nascimento Machado

A fase atual do experimento Super-Kamiokande, SK-Gd, é caracterizada pela
adição de sulfato de gadolı́nio à água do detector Cherenkov, o que melhora acapacidade de detecção de nêutrons térmicos. Para eventos de baixa energia, oprincipal canal de detecção de anti-neutrinos de elétrons é a interação de Decaimento Beta Inverso, que tem, em seu estado final, um pósitron e um nêutron. A
captura térmica de nêutrons pelo gadolı́nio emite uma cascata de raios gama de 8
MeV, melhorando a identificação dos produtos deste processo. Esta identificaçãoaprimorada reduz o background para eventos de baixa energia, permitindo até
mesmo a análise de neutrinos em energias abaixo dos limites usuais do SuperKamiokande. Uma possı́vel detecção pelo SK-Gd é a de neutrinos vindos de estrelas pré-Supernova, que correspondem ao último estado evolutivo de estrelasmassivas, logo antes do colapso do núcleo da estrela. Durante este estágio, os
processos de aniquilação de pares e decaimento beta são os principais mecanismosde resfriamento da estrela, emitindo altos fluxos de anti-neutrinos de elétrons,
cuja detecção poderia fornecer um alerta precoce de explosões de Supernovas. As
técnicas para o desenvolvimento de um sistema de alerta de pré-Supernova para o
SK-Gd são apresentadas nesta tese.

Acknowledgements
First and foremost, I would like to thank my supervisor, Gianfranca De Rosa.
From the very first day she has supported me in every single aspect during the PhD
period. She helped me establish my research goals and assisted me in every stepof the way until they were achieved. In these three years of PhD her professionaland emotional support kept me going. I could not have imagined having a better
advisor and mentor for my studies. Also a special gratitude to Vittorio Palladino
for all the help and incentive and to all my colleagues in Naples.
I would like to express gratitude to the Super-Kamiokande collaboration for
accepting my participation and contribution to the SK-Gd project. I have learned alot of fundamental physics and data analysis. Each meeting, discussion, workshop
and email exchange was valuable. My sincere thanks to Mark Vagins, LLuis MartiMagro, Alex Goldsack, and all the Gadolinium group members for the treasured
advices that were very significant in shaping my research methods and critiquing
my results. Also to Charles Simpson for introducing me to the pre-Supernova
neutrino analysis and supporting me to continue his incredible work.
I also appreciate the encouragement from my friend Guido Celentano. Not only
he made my life easier, resolving all the bureaucracies I had for living abroad, but
he always had a word of advice and care.
My appreciation goes out to my family in Brazil. To my mother, Maria Olı́mpia,
and my father, Marcelo, for the moral and emotional foundation in my life. To
my siblings Hugo, Guilherme, Mariana and Gabriel for their friendship and caring
and my sisters in law, Gabriela e Poliana. To my grandparents and my aunts Rosiand Magda, for always keeping me in their heart and prayers. And to my niece
Manuela that has already filled our lives with purpose and hope.
The realization of this PhD would have been impossible without the support
from my friends. Even living in di↵erent continents they were always there forme, giving me incentive and optimism along the way. My special gratitude goesto Bárbara, Luana, Pedro, Matheus, Rafaela, Letı́cia, Gabriel, Allan, Andressa
56
and Paulo. Also to my friends Rodrigo, Rachel, Murilo, Carla, Bruno, Eduardo,
Guilherme, Juvenal, Matheus, Paulo, Pedro, Yuji, Beatriz, Isabela and Patrı́cia.
Finally, I have been beyond fortunate to meet amazing people after moving
to Italy. Living abroad is never easy, but their company and a↵ection gave me
strength and courage to continue my journey. I am deeply grateful to Daisy, who
has followed closely and daily every single step of the way, giving me advice andmotivation to overcome obstacles and never give up. Many thanks to Brurya, forher constant caring and presence. I also wish to thank Mirko, Andy, Carol, Ally,
Kelly, and so many others for believing in me more than I believe in myself.

Contents
Declaration of Authorship

1

Abstract (English)

2

Abstract (Italian)

3

Resumo (Portuguese)

4

Acknowledgements

5

List of Figures

11

List of Tables

17

Abbreviations

19

Introduction (English)

24

Introduzione (Italian)27

Introdução (Portuguese)30

1 Neutrinos
1.1 Neutrino Oscillations in Vacuum . . . . . . . . . . . . . . . . . . . .
1.2 The MSW E↵ect . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Neutrino Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.1 Atmospheric Neutrinos . . . . . . . . . . . . . . . . . . . . .

33
34
38
39
39

7

Contents

8

1.3.2 Solar Neutrinos . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.3 Reactor Neutrinos . . . . . . . . . . . . . . . . . . . . . . .
1.3.4 Accelerator Neutrinos . . . . . . . . . . . . . . . . . . . . .
1.3.5 Astrophysical Neutrinos . . . . . . . . . . . . . . . . . . . .
Neutrino Interactions . . . . . . . . . . . . . . . . . . . . . . . . . .

43
45
47
48
50

2 Supernova and Pre-Supernova
2.1 Supernova Neutrinos . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.1 Supernova . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.2 Supernova Burst and Neutrino Emission . . . . . . . . . . .
2.1.3 SN1987A . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Di↵use Supernova Neutrino Background . . . . . . . . . . . . . . .
2.3 Pre-Supernova Neutrinos . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1 Pre-Supernova Models . . . . . . . . . . . . . . . . . . . . .

54
54
54
57
62
63
66
70

1.4

3 Super-Kamiokande with Gadolinium
76
3.1 Detector Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
3.2 Super-Kamiokande Phases . . . . . . . . . . . . . . . . . . . . . . . 81
3.3 Cherenkov Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . 82
3.4 Water Purification System . . . . . . . . . . . . . . . . . . . . . . . 86
3.4.1 Preparation for SK-Gd . . . . . . . . . . . . . . . . . . . . . 87
3.5 PhotoMultiplier Tubes . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.6 Electronics and DAQ . . . . . . . . . . . . . . . . . . . . . . . . . . 96
3.7 Trigger Conditions and WIT . . . . . . . . . . . . . . . . . . . . . . 97
3.7.1 Event Reconstruction . . . . . . . . . . . . . . . . . . . . . . 99
3.7.2 Isotropy Parameters . . . . . . . . . . . . . . . . . . . . . . 103
3.8 Calibrations in Super-Kamiokande . . . . . . . . . . . . . . . . . . . 104
3.9 Super-Kamiokande with Gadolinium . . . . . . . . . . . . . . . . . 105
3.9.1 EGADS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
3.9.2 Radiopurity . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
3.9.3 First Gadolinium Loading and Future . . . . . . . . . . . . . 114
3.9.4 Simulation of TNC on gadolinium . . . . . . . . . . . . . . . 115
4 Low Energy Inverse Beta Decay Detection in Super-Kamiokande120
4.1 Inverse Beta Decay Identification . . . . . . . . . . . . . . . . . . . 121
4.2 Backgrounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
4.2.1 Fake Neutrons and Accidental Coincidences . . . . . . . . . 125
4.2.2 Radioactive Contamination . . . . . . . . . . . . . . . . . . 127
4.2.3 Reactor Neutrinos . . . . . . . . . . . . . . . . . . . . . . . 130
4.2.4 Spallation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

Contents
4.3
4.4

9

Event Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
4.3.1 Selection of Coincidence Events . . . . . . . . . . . . . . . . 136
Multivariate Selection methods . . . . . . . . . . . . . . . . . . . . 140
4.4.1 Boosted Decision Tree . . . . . . . . . . . . . . . . . . . . . 142
4.4.2 Previous Estimations . . . . . . . . . . . . . . . . . . . . . . 146
4.4.3 BDTonline . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

5 Pre-Supernova Neutrino Sensitivity at SK-Gd
155
5.1 Previous Estimations . . . . . . . . . . . . . . . . . . . . . . . . . . 156
5.2 Results for First Gadolinium Loading . . . . . . . . . . . . . . . . . 161
5.2.1 Final Selection . . . . . . . . . . . . . . . . . . . . . . . . . 162
5.2.2 Statistical Parameters . . . . . . . . . . . . . . . . . . . . . 164
5.2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
5.3 Future Gadolinium Loading Phases . . . . . . . . . . . . . . . . . . 172
6 Pre-Supernova Alarm
177
6.1 Preliminary Considerations . . . . . . . . . . . . . . . . . . . . . . . 178
6.2 Alarm Decision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
6.2.1 False Positive Rates . . . . . . . . . . . . . . . . . . . . . . . 181
6.3 System design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
6.4 Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
7 Prospects for Low Energy detection in Hyper-Kamiokande experiment190
7.1 The mPMT concept . . . . . . . . . . . . . . . . . . . . . . . . . . 192
7.1.1 mPMT Geometry for Hyper-K . . . . . . . . . . . . . . . . . 193
7.1.2 Acrylic Vessel . . . . . . . . . . . . . . . . . . . . . . . . . . 196
7.2 mPMT Tests at Memphyno . . . . . . . . . . . . . . . . . . . . . . 197
7.3 Reduction of mPMT dark rates with BDT . . . . . . . . . . . . . . 201
7.4 Detection of Supernova and Pre-Supernova Neutrinos in Hyper-K . 206
8 Conclusion (English)

210

9 Conclusioni (Italian)213

10 Conclusão (Portuguese)

216

A Nearby pre-Supernova Candidates

219

Contents
Bibliography

10222

List of Figures
1.1
1.2
1.3

1.4
1.5

1.6
1.7
2.1
2.2

2.3

2.4

2.5

Illustration of atmospheric neutrinos production from cosmic-rays
interactions in the upper atmosphere. Reproduced directly from [17]. 40
Latest atmospheric neutrino analysis fit results in Super-Kamiokande.
These parameters will be explained in Section 1.1. From [20]. . . . . 42
Energy spectrum of Solar Neutrinos for their four most importsources: pp, 8 B, 7 Be and pep. The figure shows also the energy
thresholds for di↵erent experiments that reported solar neutrino
measurements related to each cycle. Ga: Gallium experiment, Cl:
Chlorine experiment, SK: Super-Kamiokande, and SNO: Sudbury
Neutrino Observatory. From [12]. . . . . . . . . . . . . . . . . . . . 43
Schematic view of the pp-chains and CNO nuclear fusion sequences.
From [23]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Scheme of T2K experiment showing the neutrino beam produced
at J-PARC and being detected by near detectors a few meters awayfrom production and later by Super-Kamiokande, 295 km away [6]. 48
Cross sections for low energy neutrino interactions. Reproduced
from [43]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Feynman diagram representing the Inverse Beta Decay interaction. . 52
Supernova Classification. Reproduced from [48]. . . . . . . . . . . .
Nearby core collapse Supernova candidates within 1 kpc. Coloredlabels show the star’s spectral Type. Masses and distances of thestars are shown in parenthesis. From [53]. . . . . . . . . . . . . . .
Comparison of the time integrated e↵ective anti-neutrino spectrum
from SN1987A between data from Kamiokande and IMB. Reproduced from [65]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Predicted DSNB spectrum for di↵erent models and showing thethree main backgrounds for this measurement and the energy region
where they apply. Reproduced from [65]. . . . . . . . . . . . . . . .
Characteristic onion-shape of a massive star just before the corecollapse, where the shells show the abundance of each element. Reproduced from [16]. . . . . . . . . . . . . . . . . . . . . . . . . . . .
11

55

58

63

65

67

List of Figures
2.6

2.7

2.8

2.9
3.1
3.2

Normalized spectrum (normalized energy distribution function) of
the pair-annihilation neutrinos emitted during C (solid), Ne (dashed),
O (dotted) and Si (dot-dashed) burning stage and the solar pp neutrinos (thin line), from [60]. The spectrum was calculated based on
the Monte Carlo method of [70]. . . . . . . . . . . . . . . . . . . . .
For a 20 solar masses star, the emitted anti-neutrino spectrum fordi↵erent stages. Dashed line: carbon burning, dotted line: neonburning, short-dashed line: oxygen burning, and solid line: siliconburning. Reproduced from [71]. . . . . . . . . . . . . . . . . . . . .
IBD rate in Super-Kamiokande for the 12 hours before the corecollapse for di↵erent star evolution models. It is assumed a distanceof 200 parsecs. Reproduced from [16]. . . . . . . . . . . . . . . . . .
Comparison of the considered models for the ⌫¯e luminosity andmean energy prior to the core-collapse. Reproduced from [16]. . . .

Schematic overview of the Super-Kamiokande Detector . . . . . . .
Arrangement of PMTs in modules inside the Super-Kamiokande
Detector. From [5]. . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Picture taken during the refurbishment work in 2018 of the SuperKamiokande inner detector. Copyright: Kamioka Observatory, ICRR(Institute for Cosmic Ray Research), The University of Tokyo. . . .
3.4 Spectrum of the Cherenkov radiation in water. . . . . . . . . . . . .
3.5 Scheme of the Super-Kamiokande water system. Reproduced from[5]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.6 Scheme of the EGADS water system to simulate the gadolinium
sulfate injection in Super-Kamiokande. AE = anion exchange resin,
DI = dionisation, TOC = total organic coumpound lamp, RO =reserve osmosis. Reproduced from [79]. . . . . . . . . . . . . . . . .
3.7 Water transparency measurements of EGADS compared to SKIII/IV phases. Reproduced from [79]. . . . . . . . . . . . . . . . . .
3.8 Most recent water transparency measurements in EGADS, thatfrom March 31st, 2018 started to simulate the first phase of SKGd. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.9 General scheme of the 20-inch Photo-Multiplier. From [5]. . . . . .
3.10 Quantum Efficiency for 20-inch PMT. From [5]. . . . . . . . . . . .
3.11 Picture taken during the full reconstruction of Super-Kamiokande
in 2005, showing the PMT with the Fiber Reinforced Case (FRC).
Copyright: Kamioka Observatory, ICRR (Institute for Cosmic Ray
Research), The University of Tokyo. . . . . . . . . . . . . . . . . . .

12

69

72

73
74
77
79

80
84
85

88
89

90
92
92

94

List of Figures13

3.12 Picture taken during the refurbishment work in 2018 of a new
PMT being inspected installing in Super-Kamiokande. Copyright:
Kamioka Observatory, ICRR (Institute for Cosmic Ray Research),
The University of Tokyo. . . . . . . . . . . . . . . . . . . . . . . . . 95
3.13 Likelihood values as a function of the total electron energy andopening angle between the reconstructed direction and the direction
from vertex to each PMT. From [85]. . . . . . . . . . . . . . . . . . 100
3.14 Energy resolution as a function of energy found using Monte Carloevents in [77]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
3.15 Scheme of LINAC calibration at the SK detector. The dashed line
represents the fiducial volume. The black dots indicate where thecalibration data is usually taken. From [88]. . . . . . . . . . . . . . 105
3.16 Neutron capture efficiency for di↵erent gadolinium sulfate concentrations in Super-Kamiokande. Edited from [92]. . . . . . . . . . . . 108
3.17 Scheme of EGADS detector. On the left a side view of the tank andon the right the floor and the PMT support frame. Reproduced
from [93]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
3.18 Uranium 238 decay chain, from [95]. . . . . . . . . . . . . . . . . . 111
3.19 Thorium 232 decay chain, from [95]. . . . . . . . . . . . . . . . . . . 112
3.20 Actinium 227/Uranium 235 decay chain, from [96]. . . . . . . . . . 113
3.21 Scheme of the gadolinium sulfate dissolving plan to the SuperKamiokande detector. From [92]. . . . . . . . . . . . . . . . . . . . 114
3.22 Evolution of the water transparency in the Super-Kamiokande detector and of the PMTs dark rates for the end of SK-V and firstmonths of SK-VI (after gadolinium loading). . . . . . . . . . . . . . 116
3.23 Emission energies in the two considered models for TNC on gadolinium. From [16]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
3.24 Multiplicity of -rays in the two considered models for TNC on
gadolinium. From [16] . . . . . . . . . . . . . . . . . . . . . . . . . 118
3.25 Comparison of di↵erent variables for the two models of TNC on
gadolinium in consideration. From [16]. . . . . . . . . . . . . . . . . 119
4.1

4.2

4.3

WIT efficiency compared to electron true total energy for events inthe whole ID and only inside the FV. The Cherenkov threshold and
SK-IV solar neutrino analysis threshold are showed for comparison. 123
Comparison of expected accidental coincidences events over time for
SK-IV and SK-V pure water data. For SK-V, results with improved
selection techniques are shown. These techniques will be discussed
in Section 4.4.3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
Comparison between reactor fluxes for the di↵erent years considered
to estimate the background due to reactor neutrinos. . . . . . . . . 131

List of Figures

14

4.4

Background rates for the di↵erent years in consideration. Resultswith improved techniques are shown. These techniques will be discussed in Section 4.4.3. . . . . . . . . . . . . . . . . . . . . . . . . . 131
4.5 Distribution of capture times for the fast neutrons and for neutrons
from spallation daughters. From [16]. . . . . . . . . . . . . . . . . . 133
4.6 Distribution of the energies of spallation daughters. From [16]. . . 134
4.7 Variables dR and dT for simulated signal events (black) and accidental background (red). From [16]. . . . . . . . . . . . . . . . . . . 138
4.8 Variables from BONSAI Online reconstruction (bx, by, bz, bt, bgoodness) and n18. The black solid line shows the variables before any
cut, the red dashed line shows the variables for coincidence positron
events and blue for coincidence neutron events after the Initial Search.139
4.9 Variables dR and dT calculated after the pre-selection. . . . . . . . 141
4.10 Output from the TMVA software [108] showing a random decision
tree that was used for the training for signal and background separation in the pre-Supernova neutrino analysis. Further discussions
regarding the discriminating variables used in this scheme will beheld in next sections. . . . . . . . . . . . . . . . . . . . . . . . . . . 144
4.11 ROC curves example for di↵erent classifiers. For this example, incomparison to the Boosted Decision Tree (BDT) ROC curve, are
the Fisher Linear Discriminant (Fisher) and the rectangular cuts
(Cuts). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
4.12 ROC curves from reproduced BDT used in [16] (black), the new
BDT with o✏ine variables used for Final Selection (blue) and twodi↵erent trials to get the best performance out of a training using
only online variables (green and yellow). . . . . . . . . . . . . . . . 151
4.13 Background and Signal separation for the BDT with online variables.152
5.1
5.2
5.3
5.4

Distribution of the variables from the DC channel in the final selection for [16] results. . . . . . . . . . . . . . . . . . . . . . . . . . 157
Distribution of the BDT score for the single channel in the final
selection for [16] results. . . . . . . . . . . . . . . . . . . . . . . . . 157
Distribution of variables in the coincidence channel. Solid lines show
before the final selection and dashed lines after. . . . . . . . . . . . 163
Evolution of significance level for the normal ordering neutrino massmodels for stars at 200 pc in the last hours before the core-collapse
at SK-Gd with 0.02% Gd2 (SO4 )3 · 8H2 O. . . . . . . . . . . . . . . . 170

List of Figures
5.5

5.6

6.1
6.2
6.3
6.4
6.5

6.6

7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8

7.9

15

Expected early warning above 3 against the distance for 15 M
and 25 M stars for the considered pre-Supernova models, evaluatedin steps of 0.01 kparsec. Dashed line shows predictions using the
GLG4SIM -ray model and solid line for the ANNRI -ray model
evaluated over a 7-hour window. . . . . . . . . . . . . . . . . . . . . 171
Expected early warning considering a rate of 3.2 Supernovae per
century against the distance for 15 M and 25 M stars for theconsidered pre-Supernova models, evaluated in steps of 0.01 kparsec. Dashed line shows predictions using the GLG4SIM -ray modeland solid line for the ANNRI -ray model evaluated over a 7-hour
window. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
Edited from [83]. Illustration of the acquisition system in SuperKamiokande and where the pre-Supernova alarm is placed. . . . . . 179
Screenshot of the main menu of the alarm system after initialization.183
Screenshot of the alarm view when data is being processed. Information regarding last evaluations are also showed in the screen. . . 183
Main web page of the alarm system used to verify latest evaluations
and the alarm status. . . . . . . . . . . . . . . . . . . . . . . . . . . 185
Screenshot of the secondary web page showing plots for the evolution of evaluated significance levels and selected IBD pairs fordi↵erent time intervals. . . . . . . . . . . . . . . . . . . . . . . . . . 187
Screenshot of the secondary web page showing plots of di↵erent
variable distributions over the last 12 hours for the events selectedby the system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
Illustrated scheme of the Hyper-K Detector. . . . . . . . . . . . . . 191
Illustration of the mPMT module. . . . . . . . . . . . . . . . . . . . 194
The two initial prototypes for Hyper-K from INFN (left) and TRIUMF (right). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
Assembling of the mPMT prototype in INFN Naples. . . . . . . . . 195
Acrylic samples transmittance . . . . . . . . . . . . . . . . . . . . . 197
Picture of the MEMPHYno test bench at APC in Paris, where themPMT prototype developed by INFN Naples is being tested. . . . . 198
Results of dark rate evaluation of di↵erent PMTs in the mPMT
module prototype tests at MEMPHYno. . . . . . . . . . . . . . . . 200
Vertex resolution (top) and electron/muon separation (bottom) improved by the use of 3-inch PMTs (red) in comparison with 20-inch(blue) and 8-inch (black). From [122]. . . . . . . . . . . . . . . . . . 202
ROC curves for di↵erent trials of BDT trainings to be used for thesoftware dark rate reduction of mPMTs. . . . . . . . . . . . . . . . 204

List of Figures16

7.10 Signal and background separation for the most efficient BDT of
Figure 7.9. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
7.11 Predicted Inverse Beta Decay reactions due to Supernova Relic Neutrinos in function of years for current and planned experiments, from[128]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

List of Tables
2.1

Approximate duration of burning stages for a 20 M star and thefraction and average energy of electron neutrinos emitted by pairannihilation [69]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

3.1
3.2

Trigger schemes in Super-Kamiokande. . . . . . . . . . . . . . . . . 97
Properties of Gd isotopes, in comparison with Hydrogen. From theinformation in [79]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

4.1

Radioisotopes activity level required for the Supernova Relic Neutrino and Solar Neutrino analyses. . . . . . . . . . . . . . . . . . . . 128
Rates for backgrounds per hour due to radioisotopes contamination
on gadolinium sulfate. Requirements for the current analysis areshown in Table 4.1. Previous requirements for contamination di↵er
from current ones for 235 U < 3mBq/kg. . . . . . . . . . . . . . . . . 130
Ranked importance of the used online variables. . . . . . . . . . . . 152
Approximate time spent to process data for previous approach andoptimized (with BDTonline ). . . . . . . . . . . . . . . . . . . . . . . 153

4.2

4.3
4.4
5.1

5.2
5.3
5.4
5.5

Number of expected signal events from pre-Supernova star at 200
parsecs in the final 12 hours before core-collapse at SK-Gd with
0.2% Gd2 (SO4 )3 · 8H2 O, from previous estimations [16]. . . . . . . . 158
Maximum range of detection of pre-Supernova stars by SK-Gd with
0.2% Gd2 (SO4 )3 · 8H2 O, from previous estimations [16]. . . . . . . . 159
Expected early warning in SK-Gd with 0.2% Gd2 (SO4 )3 · 8H2 O,from previous estimations [16]. . . . . . . . . . . . . . . . . . . . . . 160
Remaining background events per 7 hour window for the DC events
after final selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
Number of expected signal events from pre-Supernova star at 200
parsecs in the final 7 hours before core-collapse at SK-Gd with
0.02% Gd2 (SO4 )3 · 8H2 O. Results are shown for two -ray emission models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168

17

List of Tables

18

5.6

Maximum range of detection of pre-Supernova stars by SK-Gd with
0.02% Gd2 (SO4 )3 ·8H2 O, for normal ordering neutrino mass models.
Results are shown for two -ray emission models evaluated over a
7-hour window. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
5.7 Expected early warning in SK-Gd with 0.02% Gd2 (SO4 )3 ·8H2 O, forBetelgeuse-like models with normal ordering neutrino mass models.
Results are shown for two -ray emission models evaluated over a
7-hour window. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
5.8 Expected rates for the Spontaneous Fission and Pairs of Neutrons
backgrounds for the three phases of SK-Gd. . . . . . . . . . . . . . 173
5.9 Maximum range of detection of pre-Supernova stars by SK-Gd with
0.06% Gd2 (SO4 )3 ·8H2 O, for normal ordering neutrino mass models.
Results are shown for two -ray emission models evaluated over a
7-hour window. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
5.10 Maximum range of detection of pre-Supernova stars by SK-Gd with
0.2% Gd2 (SO4 )3 · 8H2 O, for normal ordering neutrino mass models.
Results are shown for two -ray emission models evaluated over a
7-hour window. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
5.11 Expected early warning in SK-Gd with 0.06% Gd2 (SO4 )3 ·8H2 O, forBetelgeuse-like models with normal ordering neutrino mass models.
Results are shown for two -ray emission models evaluated over a
7-hour window. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
5.12 Expected early warning in SK-Gd with 0.2% Gd2 (SO4 )3 · 8H2 O, forBetelgeuse-like models with normal ordering neutrino mass models.
Results are shown for two -ray emission models evaluated over a
7-hour window. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
6.1

Estimated early warning lead time of the pre-Supernova alert system.186

7.1

Variables in order of importance of the BDT for the software Dark
Rate reduction in Hyper-Kamiokande. . . . . . . . . . . . . . . . . 205
Expected maximum range of detection of pre-Supernova stars inHyper-K loaded with 0.02% Gd2 (SO4 )3 · 8H2 O, for normal ordering
neutrino mass models. . . . . . . . . . . . . . . . . . . . . . . . . . 209
Expected early warning in Hyper-K loaded with 0.02% Gd2 (SO4 )3 ·8H2 O, for Betelgeuse-like models with normal ordering neutrinomass models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209

7.2

7.3

A.1 List of pre-Supernova candidates with estimated masses and distances. Reproduced from [53]. . . . . . . . . . . . . . . . . . . . . . 221

Abbreviations
M

Solar Masses

Ac

Actinium

Adaboost

Adaptive boost

AmBe

Americium-Beryllium

BDT

Boosted Decision Tree

BDTonline

Boosted Decision Tree with online variables

BONSAI

Branch Optimization Navigating Successive Annealing Iterations

C

CarbonCC

Charged Current

CCSN

Core-Collapse SuperNova

DAQ

Data AcQuisition

DC

Delayed Coincidence

DSNB

Di↵use Supernova Neutrino Background

EGADS

Evaluating Gadolinium’s Action on Detector Systems

Fe

Iron

FPR

False Positive Rate

FRP

Fibre Reinforced Plastic

FV

Fiducial Volume

H

HydrogenHK

Hyper-Kamiokande19Abbreviations

20

HPG

Hyper-Pure Germanium

IBD

Inverse Beta Decay

ID

Inner Detector

IWCD

Intermediate Water Cherenkov Detector

JUNO

Jiangmen Underground Neutrino Observatory

LD

Fisher’s Linear Discriminant

LEAF

Low Energy Algorithm Framework

LMC

Large Magellanic Cloud

MO

Mass OrderingmPMT

multi-PMTMSW

Mikheyev-Smirnov-WolfensteinN

NitrogenNC

Neutral Current

Ne

Neon

NSE

Nuclear Statistical Equilibrium

O

OxygenOD

Outer Detector

PE

PhotoElectron

PMT

PhotoMultiplier Tube

QE

Quantum Efficiency

ROC

Response Operator Characteristic

RSG

Red SuperGiant

SF

Spontaneous Fission

Si

SiliconSK

Super-Kamiokande

SMStandard Model Physics

SN

SuperNova

SNe

SuperNovaeAbbreviations

21

SRF

Supernova Relic FluxTh

Thorium

TNC

Thermal Neutron Capture

U

Uranium

WC

Water-Cherenkov

WCSim

Water Cherenkov Simulator

WIT

Wideband Intelligent Trigger

ZAMS

Zero Age Main SequenceTo my parents. . .

22

Introduction (English)

In 2020, the Super-Kamiokande experiment has moved to its next stage, SKGd, in which gadolinium sulfate was added to the water in the detector. This
improves the capability of neutron identification, which has great implications forthe detection of low energy neutrinos. Approximately 13 tons of gadolinium sulfate
was dissolved to achieve the initial phase of 0.02% concentration by mass.
The observation of astronomical objects beyond our solar system by neutrino
detection is called Neutrino Astronomy and it was initiated with the first observation of neutrinos from Supernova in 1987 by the Kamiokande experiment. SK-Gd
has the potential to contribute significantly for Neutrino Astronomy, observing
neutrinos from di↵erent astronomical sources such as pre-Supernova stars.
During the last stages of the evolution of massive stars, which have mass greaterthan eight solar masses, the production of neutrino and anti-neutrino pairs become
main stellar cooling mechanism. This characteristic behaviour goes over for a few
hours preceding the core-collapse supernova. The emitted electron anti-neutrino
24

25
exceeds the threshold for inverse beta decay in hydrogen, so that detection would
be possible in Super-Kamiokande. Neutrons, which result from the inverse beta
decay, are subjected to capture by Hydrogen nuclei, emitting gamma-rays withan energy of 2.2 MeV. However, for gadolinium, the neutron capture would emit
gamma ray cascades with approximately 8 MeV. SK-Gd is characterized for having
reduced backgrounds and enhanced efficiency for neutron tagging. The experimentis now capable of detecting neutrinos emitted in the final hours of massive star
lives, known as pre-Supernova Neutrinos.
The detection of pre-Supernova neutrinos could provide an early warning for
Supernova events, sending alerts to the community in case of a potential corecollapse Supernova within a few hours. The goal of this project is to create an
alarm system for Supernovae based on the detection of pre-Supernova neutrinos.
This system performs a real time search for inverse beta decay candidates on low
energy events in SK-Gd and apply the necessary statistical tests, saving long term
information to make alarm decisions.
In this thesis the latest results of detection capabilities of pre-Supernova Neutrinos at Super-Kamiokande with Gadolinium will be presented. Chapters 1 and 2 introduce physical concepts of Neutrino physics and Supernova and Pre-Supernova.
Chapter 3 discusses about the Super-Kamiokande detector and the characteristics
of its new phase, SK-Gd. In Chapter 4, the backgrounds for the pre-Supernova

26
Neutrino detection will be shown, as well as the multivariate techniques used forevent selection.
Chapters 5 and 6 will present the improved models and sensitivities for preSupernova Neutrino detection and the application of this study, which is the preSupernova alert system.
At last, Chapter 7 will show prospects of Supernova and pre-Supernova neutrino
detection in next generation experiment Hyper-Kamiokande.

Introduzione (Italian)Nel 2020, l’esperimento Super-Kamiokande ha iniziato una nuova fase sperimentale, SK-Gd, aggiungendo polvere di gadolinio alle 50.000 tonnellate di acqua
ultrapura del rivelatore. Ciò migliora la capacità di identificazione dei neutroni,
con importanti implicazioni per la rilevazione di neutrini di bassa energia. Circa
13 tonnellate di solfato di gadodinio sono state utilizzate per ottenere, in questa
prima fase, la concentrazione percentuale in massa dello 0,02%.
L’osservazione dei neutrini da sorgenti astrofisiche, chiamata Astronomia Neutrinica, è nata con la prima osservazione di neutrini da Supernova nel 1987 dall’esperimento
Kamiokande, estendendo l’astronomia classica oltre la radiazione elettromagnetica. Il progetto SK-Gd ha il potenziale per contribuire in modo significativo
all’Astronomia Neutrinica, osservando i neutrini provenienti da diverse sorgenti
astrofisiche.
Durante le ultime fasi dell’evoluzione delle stelle massicce, che hanno cioè massa
maggiore di otto masse solari, la produzione di neutrini e antineutrini diventa il27

28
principale meccanismo di ra↵reddamento. Questa emissione caratteristica si protrae per alcune ore prima del collasso del nucleo della supernova. Gli antineutrini
elettronici emessi in questa fase hanno energia sufficiente per il processo di decadimento beta inverso in idrogeno, e la loro rivelazione è quindi possibile in SuperKamiokande. I neutroni prodotti nel decadimento beta inverso, sono catturati dai
nuclei di idrogeno, emettendo raggi gamma con un’energia di 2,2 MeV. Il processo
di cattura neutronica con il gadolinio produce una cascata di raggi gamma con
energia di circa 8 MeV, migliorando le possibilità di rivelazione.
L’attuale fase sperimentale SK-Gd è quindi caratterizzata da una maggiore efficienza per l’identificazione dei neutroni permettendo la rivelazione di neutrini
emessi nelle ultime ore di vita di stelle massicce, noti come neutrini da preSupernova. La rivelazione di neutrini da pre-Supernova potrebbe fornire alla comunità astrofisica un allarme immediato e affidabile al verificarsi di una Supernova
nella Galassia.
L’obiettivo di questo lavoro di tesi è appunto la realizzazione di un sistema diallarme per Supernovae basato sulla rilevazione di neutrini da pre-Supernova. Per
sviluppare questo sistema, è realizzata una ricerca in tempo reale di eventi di bassa
energia di decadimento beta inverso in SK-Gd e il sistema di allarme è basato sullo
studio della significatività statistica.
In questa tesi sono presentati gli ultimi risultati sulla capacità di rivelazione di29
neutrini da pre-Supernova in Super-Kamiokande con Gadolinio. I capitoli 1 e 2
introducono i concetti principali della fisica dei neutrini, delle Supernova e preSupernova. Il capitolo 3 descrive il rilevatore Super-Kamiokande e le caratteristichedell’attuale fase sperimentale SK-Gd. Nel Capitolo 4 sono discussi le sorgenti
di background per la rivelazione di neutrini da pre-Supernova e gli algoritmi dianalisi multivariata utilizzati per la selezione degli eventi. Nei capitoli 5 e 6 sonopresentati i modelli e le sensibilità sperimentali per la rivelazione di neutrini da
pre-Supernova e lo sviluppo del sistema di allarme.
Infine, il Capitolo 7 presenta le prospettive di rivelazione di neutrini da Supernova e pre-Supernova nel futuro esperimento Hyper-Kamiokande.

Introdução (Portuguese)

Em 2020, o experimento Super-Kamiokande passou para sua próxima fase, SKGd, no qual o sulfato de gadolı́nio foi adicionado à água no detector, melhorando
a capacidade de identificação de nêutrons, o que tem grandes implicações para adetecção de neutrinos de baixa energia. Aproximadamente 13 toneladas de sulfato
de gadolı́nio foram dissolvidas para atingir a fase inicial de 0,02% de concentraçãoem massa.
A observação de objetos astronômicos, para além do nosso sistema solar, pordetecção de neutrinos é conhecida como ”Astronomia de Neutrinos” e foi iniciada
com a primeira observação de neutrinos de Supernova em 1987, pelo experimento
Kamiokande. SK-Gd tem o potencial de contribuir significativamente para a Astronomia de Neutrinos, observando neutrinos de diferentes fontes astronômicas,
como estrelas pré-Supernova.
Durante os últimos estágios de evolução das estrelas massivas, que são estrelascom massas superiores a oito massas solares, a produção de pares de neutrinos e
30

31
anti-neutrinos torna-se o principal mecanismo de resfriamento estelar. Este comportamento caracterı́stico continua por algumas horas antes do colapso do núcleo
da estrela. Os anti-neutrinos de elétrons emitidos excedem o limite para o Decaimento Beta Inverso no hidrogênio, de modo que tal detecção seria possı́velno Super-Kamiokande. Os nêutrons, que resultam do Decaimento Beta Inverso,são submetidos à captura por núcleos de hidrogênio, emitindo raios gama com
energia de 2,2 MeV. No entanto, para o gadolı́nio, a captura de nêutrons emite
cascatas de raios gama com aproximadamente 8 MeV. SK-Gd é caracterizado por
ter backgrounds reduzidos e eficiência aprimorada para a identificação de nêutrons.
Atualmente, o experimento é capaz de detectar neutrinos emitidos nas horas finaisda vida de estrelas massivas, conhecidos como neutrinos pré-Supernova.
A detecção de neutrinos pré-Supernova pode fornecer um alerta precoce parafenômenos de Supernova, enviando alertas para a comunidade no caso de um
possı́vel colapso do núcleo de uma estrela dentro de algumas horas. O objetivo
deste projeto é criar um sistema de alarme para Supernovas baseado na detecçãode neutrinos pré-Supernova. Este sistema realiza uma busca em tempo real por
candidatos de Decaimento Beta Inverso em eventos de baixa energia no SK-Gd e
aplica os testes estatı́sticos necessários, salvando informações de longo prazo para
tomar as decisões de envio de alertas.
Nesta tese, serão apresentados os resultados mais recentes das capacidades de32detecção de Neutrinos pré-Supernova no Super-Kamiokande com Gadolı́nio. Os
capı́tulos 1 e 2 introduzem conceitos fı́sicos de Fı́sica de Neutrino e Supernova ePré-Supernova. O Capı́tulo 3 discute sobre o experimento Super-Kamiokande e
as caracterı́sticas de sua nova fase, SK-Gd. No Capı́tulo 4, os backgrounds paraa detecção pré-Supernova Neutrino serão mostrados, bem como as técnicas deestatı́stica multivariável usadas para a seleção de eventos.
Os capı́tulos 5 e 6 apresentarão os modelos aprimorados e as sensibilidades paraa detecção de neutrinos pré-Supernova e a aplicação deste estudo, que é o sistemade alerta de pré-Supernova.
Por fim, o Capı́tulo 7 mostrará as perspectivas de detecção de neutrinos de Supernova e pré-Supernova no experimento de próxima geração Hyper-Kamiokande.

Chapter 1

Neutrinos

The main candidates to reveal new physics, Neutrinos, are currently the focus
of several experiments worldwide dedicated to study these mysterious particles.
The initial concept of neutrinos was introduced in 1930 by W. Pauli as being aneutral particle to explain missing energy in beta decays [1]. Later in 1933 they
were given the name of neutrinos by E. Fermi [2]. Their first observation was in1956 by C. Cowan and F. Reines [3]. Since then, many experiments were design
to study their properties (see, e.g., [4] and references therein). Currently, SuperKamiokande [5], T2K [6], SNO [7], IceCube [8], and many other experiments are in
the search of Neutrino’s oscillation parameters and masses. The understanding of
their physical properties might prove the matter-antimatter asymmetry as givenby the CP violation [9; 10; 11].
33

Neutrinos

34

Some examples of neutrinos coming from natural sources include: the ones
from the decay of the products of cosmic ray interactions with the atmosphere(Atmospheric Neutrinos), from nuclear reactions in the Sun (Solar Neutrinos), or,
the one with most importance to this thesis, generated from astrophysical sources.
More specifically, neutrinos from the di↵erent stages before and after the explosionof massive stars, known as Supernova, are the object of this study.
Neutrinos can also be produced artificially, like in the T2K experiment, in which
the production of neutrinos are of big importance since it is known the production
properties such as direction, energy, flavor, etc.
The following sections will introduce di↵erent sources of neutrinos, current
experiments and physical properties.

The discussion of Supernovae and pre-Supernova Neutrinos will be the focus of next chapter.

1.1

Neutrino Oscillations in Vacuum

Neutrinos exist in three flavours in the Standard Model, in which each one of them
forms a doublet with the associated charged lepton. They are represented by the
weak-eigenstates ⌫e , ⌫µ and ⌫⌧ .
These eigenstates are usually written as linear superposition of three mass eigenstates ⌫1 , ⌫2 and ⌫3 , with respective masses m1 , m2 and m3 . The relation betweenNeutrinos

35

mass and weak eigenstates is given by Equation (1.1), in which the unitary matrix U corresponds to the Pontecorvo-Maki-Nakagawa-Sakata (PMNS) matrix, or
neutrino mixing matrix [12].

|⌫↵ i =

Xj⇤U↵j|⌫j i,(1.1)

where flavour eigenstates are represented by greek letters and mass eigenstates bylatin letters.
The neutrino mixing matrix can be parametrized by three mixing angles ✓12 ,✓13 and ✓23 , and a CP violating phase .
0

B1
B
B
U =B
B0
B
@
0

0
c23
s23

10

0 C B c13
CB
CB
B
s23 C
CB 0
CB
A@
c23
s13 ei0 s13 e
1

0

0

c13

0

c12 c13
B
B
B=B
B s12 c23 c12 s13 s23 ei
B@s12 s23 c12 s13 s23 ei

i

10

1

C B c12 s12 0C
CB
C
CB
C
CB s
C
C B 12 c12 0C
CB
C
A@
A
0
0 1

s12 c13c12 c23s12 s13 s23 ei

c12 c23s12 s13 c23 ei

s13 e

i

(1.2)

1

C
C
C
c13 s23 CC,CA
c13 c23

where cab = cos(✓ab ) and sab = sin(✓ab ), which (a, b) = (1, 2, 3), corresponding to
the mass eigenstates. This parametrization is written in a way that, without theNeutrinos

36

CP violating phase, it is equivalent to a rotation in three dimensions.
If Neutrinos are Majorana particles, two new phases need to be added to (1.2).
However, the Majorana phases have no e↵ect when looking into neutrino oscillations, since they cancel out when evaluating the flavour evolution [12].

0

B1
B
B
U =B
B0
B
@
0

0
c23
s23

10

0 C B c13
CB
CB
B
s23 C
CB 0
CB
A@
c23
s13 ei10

10 s13 e i C B c12 s12 0C
CB
C
CB
C
B
C
1
0 C
C B s12 c12 0C ⇥
CB
C
A@
A
0
c13
0
0 1
0

(1.3)

1

0
0C
Bei↵1 /2
B
C
B
C
B
⇥B 0
ei↵2 /2 0C
C
B
C
@
A
0
0
1
When studying neutrino oscillations in vacuum, the probability of a neutrino
with flavor ”↵” to become a neutrino with flavor ” ” when travelling a distanceNeutrinos

37”L”, is given by the oscillation probability (1.4) [12].

P (⌫↵ ! ⌫ ) =

X

2
2 L
⇤
U j U↵je imj 2E

j

=

X

j=1,3

+

X

⇤
2Re[U j U ⇤k U↵jU↵k ]cos

j<k

+

X

|U j |4 |U↵j |4 +✓⇤2Im[U j U ⇤k U↵jU↵k ]sin

j<k

✓

(1.4)

◆

+

m2jk L
2E,

m2jk L
2E◆

where E is the neutrino energy.
An important definition arising from equation (1.4) is of the squared mass difference

m2jk = m2k

m2j . A still open question in Neutrino Physics is related towhether the eigenstate m3 is lighter or heavier than m1 and m2 . So, measurementsof oscillation parameters by experiments with known length ”L” and energy ”E”
are very important to answer questions about the Neutrino Mass Hierarchy [13].
Usually, models with the mass hierarchy m1 < m2 < m3 , orm223 positive, arecalled Normal Ordered models, and for m1 < m3 < m2 , orm223 negative, arecalled Inverted Ordered models. This will be of great importance for the models
used for this work to predict di↵erent pre-Supernova Neutrinos fluxes.
When applying CP transformation to equation (1.4), in order to obtain thecorrespondent probability for anti-neutrino oscillation, we see that the oscillation

Neutrinos

38

probability will only violate CP if the phasemeasurements of the phaseis di↵erent from 0 or ⇡. So, again,

are important for the studies of CP violation in the

lepton sector and understanding the matter-antimatter asymmetry in the Universe.
More details can be found in the review work [14] and references therein.

1.2

The MSW E↵ect

The Neutrino oscillations introduced in last section happen in vacuum. However,when neutrinos propagate through matter, they will be a↵ected by interactions,changing the pattern of neutrino oscillations. This change occurs because electron
neutrinos can interact with electrons in matter through both neutral current (NC)and charged current (CC), while the other neutrino flavors, muon and tau, canonly interact through NC. This e↵ect of oscillations in matter is called MikheyevSmirnov-Wolfenstein (MSW) e↵ect [15].
For Neutrinos coming from stars, the path from the core to the surface will leadto resonant conversion between neutrino flavours [16].
The Hamiltonian describing a charged current interaction, mediated by the Wboson, will have an additional term, leading to the e↵ective mass term:p
A = 2 2(GF Ye /mn )⇢E,

(1.5)

Neutrinos

39

where Ye is the electron fraction of the matter, mn is the nucleon mass, ⇢ is thedensity and GF is the Fermi’s constant.
For the two flavours oscillation case, the e↵ective mass splitting will bep(( m2 )cos(2✓)A)2 + (( m2 )sin(2✓))2(1.6)

The resonance occurs at the density:( m2 )cos(2✓)
p⇢=
2E 2GF

The MSW resonance in the Sun depends on(1.7)

m221 and it is called low resonanceand doesn’t apply to anti-neutrinos, which is di↵erent than the high resonancebym232 , which applies to anti-neutrinos so it is relevant for the pre-Supernova

studies [16].

1.3

Neutrino Sources

1.3.1

Atmospheric Neutrinos

Protons and heavy nuclei arriving from anywhere in the Universe to the Earth are
known as Cosmic Rays. These particles come in an isotropic and constant flux.

Neutrinos

40

Figure 1.1: Illustration of atmospheric neutrinos production from cosmic-rays
interactions in the upper atmosphere. Reproduced directly from [17].

When they interact with the upper atmosphere, they typically produce unstablemesons called Pions ⇡ and Kaons K, which decay into other particles, including
neutrinos. We call them Atmospheric Neutrinos.

Neutrinos

41

The possible decays are:K + ! µ+ + ⌫µ ! e+ + ⌫¯e + ⌫µ
K ! µ + ⌫¯µ ! e + ⌫e + ⌫¯µK + ! ⇡+ + ⇡0
K + ! ⇡ + ⇡0
K + ! ⇡+ + ⇡+ + ⇡+K !⇡ +⇡ +⇡

(1.8)
+

K + ! ⇡ 0 + e+ + ⌫e
K + ! ⇡ 0 + e + ⌫¯e⇡ + ! µ+ + ⌫µ ! e+ + ⌫¯e + ⌫µ
⇡ ! µ + ⌫¯µ ! e + ⌫e + ⌫¯µThe computation of these neutrino fluxes is very difficult, once it needs to take
into account, among other factors, the di↵erent composition of Earth’s atmospheredepending on the location, the e↵ects from the Sun on cosmic rays and the different channels of production. Also, the energy of the neutrinos depends on theoriginal cosmic ray, which increases significantly their energy range. See the review
work [18] and references within for more information regarding the calculation ofneutrino fluxes.

Neutrinos

42

Figure 1.2: Latest atmospheric neutrino analysis fit results in SuperKamiokande. These parameters will be explained in Section 1.1. From [20].

The observation of atmospheric neutrinos was responsible for the first indicationof neutrino oscillations [19]. Figure 1.2 shows the latest fit results for oscillation
parameters in Super-Kamiokande [20].
Experiments studying atmospheric neutrinos are: Super-Kamiokande [5], SNO[7], MINOS [21], and IceCube [8]. The energy range in which they are observed isfrom 100 MeV to higher than 10 TeV [17].

Neutrinos

43

Figure 1.3: Energy spectrum of Solar Neutrinos for their four most importsources: pp, 8 B, 7 Be and pep. The figure shows also the energy thresholds
for di↵erent experiments that reported solar neutrino measurements related toeach cycle. Ga: Gallium experiment, Cl: Chlorine experiment, SK: SuperKamiokande, and SNO: Sudbury Neutrino Observatory. From [12].

1.3.2

Solar Neutrinos

Neutrinos are also produced in thermonuclear reactions inside the stars. For theSun, di↵erent spectra of neutrinos are emitted depending which ”cycle” of reaction
is burning Hydrogen.
Solar neutrinos fluxes are predicted by the Solar Standard Model (SSM) developed by J. Bahcall [22]. Electron neutrinos are products of thermonuclear fusions
in the solar core, which is how our Sun obtains its thermal energy. Figure 1.4
shows a schematic view of the pp-chains and CNO cycles of the Sun. In summary,Neutrinos

44

Figure 1.4: Schematic view of the pp-chains and CNO nuclear fusion sequences. From [23].

the e↵ective fusion reaction, which proceeds through all these chains and cycles,
is given by:
4p + 2e ! 4 He + 2⌫e + 26.73M eV(1.9)

Estimations to the time the heat takes to travel from the center to the surface of
the Sun are uncertain, between 100,000 to 1,000,000 years. However, the generated
neutrinos reach the Earth in only 8 minutes approximately.
The flux of electron neutrinos that reaches the Earth is of 6⇥1010 (cm2 s) 1 . Theenergy produced by the solar nuclear reactions will be shared among the particles
in the final state. Neutrinos are capable of escaping from the Sun, carrying partof this energy. Since they are produced through di↵erent cycles and chains, itNeutrinos

45

implies di↵erent neutrino energy distributions. Figure (1.3) shows di↵erent energy
spectrum related to those cycles and chains, as well as which experiments reported
the measurements related to each one of them.
The combined observations from Super-Kamiokande and SNO in 2001 gave evidence for the neutrino oscillations. Super-Kamiokande used neutrino-electron elastic scattering, ⌫e +e ! ⌫e +e , to measure the recoil of electrons and zenith angle
dependence of solar neutrinos, while SNO measured neutrino-deuteron chargedcurrent reaction rate [24], ⌫e + d ! ⌫e + p + p, and exploit its unique capability toobserve neutral current interactions, evaluating the flux of all neutrino types [25].
These measurements combined provided evidence of electron neutrino oscillation
into other flavors of neutrinos.

1.3.3

Reactor Neutrinos

Reactors are currently one of the main artificial sources of neutrinos. It is acontrollable source of electron anti-neutrino with low energy and it has a great
importance for the topic of this thesis, since it is the main background for thedetection of pre-Supernova neutrinos.
The fission of Uranium and Plutonium isotopes 235 U, 239 Pu, 238 U and 241 Pu iswhat give power to Reactors. It produces lighter elements, which will also decay

Neutrinos

46

and contribute to the energy. The beta decay of products is the source of electron
anti-neutrinos.
The energy range of reactor neutrinos is 1 to 10 MeV, with mean energy peakaround 4 MeV. Early experiments to study this kind of neutrinos, as IIL [26],Bugey [27], Bugey-3 [28], Palo Verde [29], and Chooz [30], performed importantmeasurements of reactor neutrino spectrum and oscillation parameters. Recent
experiments were designed to achieve even more precise measurements: Daya Bay[31], Double Chooz [32], RENO [33], among others. One of the next generation experiments, JUNO [34], will further improve the detection capabilities to use reactor
neutrinos to determine neutrino mass ordering and more precise measurements ofoscillation parameters [35].
The detection of reactor neutrinos is of great impact for oscillation parameters
estimations.
More details about Reactor Neutrino and current reactors in Japan will be
discussed later when reviewing backgrounds for the low energy detection in SuperKamiokande with Gadolinium.

Neutrinos

1.3.4

47

Accelerator Neutrinos

Another main source of artificial neutrinos is the accelerator neutrinos. Neutrino
beams are created to travel a fixed distance. For that, protons are accelerated
and hit a target, producing new particles which will decay into neutrinos. The
experiments have the tuning power to create both neutrinos and anti-neutrinos,
using magnets to select positive or negative pions created by the interaction of
protons with the target. Depending on the distance between the source of thebeam and the detector, experiments are defined as Short-baseline or Long-baseline.
Short-baseline experiments include MicroBooNE [36], MINERvA [37], etc. They
have detectors close to the neutrino beam source and study anomalous neutrino appearance and disappearance in regions where the oscillation probability is expected
to be small. They often serve as near detectors for long-baseline experiments.
Long-baseline experiments include T2K [6] and NOvA [38], which measure theneutrinos after travelling a long distance, understanding neutrino mixing in regions
where large oscillation probability.
The T2K (Tokai-to-Kamioka) experiment is a long-baseline neutrino experiment
located in Japan, illustrated by Figure (1.5). Protons are accelerated at the accelerator complex J-PARC (Japan Proton Accelerator Research Complex), hittinga graphite target to produce the neutrino or anti-neutrino beam. T2K has near

Neutrinos

48

Figure 1.5: Scheme of T2K experiment showing the neutrino beam produced
at J-PARC and being detected by near detectors a few meters away from production and later by Super-Kamiokande, 295 km away [6].

detectors 280 meters from the beam production, in order to study neutrinos before
the oscillation. The near facility contains an on-axis detector, the Interactive Neutrino GRID (INGRID) and an o↵-axis detector, ND280, to measure the neutrino
beam director and flux, characterizing the beam before any oscillation occurred.
The beam then travels for 295 km to reach the far detector, Super-Kamiokande.
The T2K experiment, using a combined analysis of appearance and disappearance of both neutrino and anti-neutrino channels, has established a constraint on
the value of

1.3.5

CP with 99.73% confidence level [39].

Astrophysical Neutrinos

Since neutrinos are charge-less particles, they can travel without any major significant deflections in the Cosmos. It means that they can be used as an importantNeutrinos

49

source for studying our Universe. The observation of astrophysical neutrinos by
the Kamiokande experiment in 1987 introduced the Neutrino Astronomy [40].
Astrophysical Neutrinos can come from Supernovae, Active Galactic Nuclei,
Neutron Stars, Black Holes, etc.
In 1987, the first detection of extra-solar neutrino was made with the Supernova SN1987A in the Large Magellanic Cloud. In 2013, the experiment IceCube
detected very high energy neutrinos with energies between 10 TeV and 2 PeV[41; 42].
The Neutrino Astronomy also contributes to the cross-checks among di↵erent
sources of particles coming from the Cosmos, such as Cosmic Rays, Gravitational
Waves, and Photons, bringing us to a multi-messenger era in astrophysics.
Neutrinos coming from Supernova are of main importance for this thesis. Morespecifically, the detection of neutrinos emitted in the few latest hours of the stars
lives before the explosion, will allow to create a Supernova alert, allowing di↵erent
experiments to be prepared for its detection. The discussion of astrophysicalneutrinos from Supernova and pre-Supernova will be continued in the next chapter.

Neutrinos

50

Figure 1.6: Cross sections for low energy neutrino interactions. Reproduced
from [43].

1.4

Neutrino Interactions

When considering the neutrino detection, one important characteristic to be taken
into account is the interaction cross-section. Depending on which energy range
neutrinos are being studied, di↵erent interactions will be dominant, so particularcross-sections need to be studied for each case. For ultrahigh energies (above 100
GeV), the main interaction is the deep inelastic scattering and the cross sections
are well known. For intermediate energies (around 1 GeV), dominant processes
are: quasi-elastic scattering ⌫ + n ! l + p or ⌫¯ + p ! l+ + n, where l is the
produced charged lepton, and single pion production, which is given by di↵erent

Neutrinos

51

processes:⌫l + p ! l + p + ⇡ +⌫l + n ! l + p + ⇡ 0⌫l + n ! l + n + ⇡ +(1.10)+⌫¯l + p ! l + p + ⇡⌫¯l + p ! l+ + n + ⇡ 0⌫¯l + n ! l+ + n + ⇡For the lower energy region, below 100 MeV, the dominant interaction processes
are elastic scattering, charged current ⌫e + (N, Z) ! e + (N(N, Z) ! e+ + (N + 1, Z

1, Z + 1), ⌫¯e +1) and neutral current ⌫ + A ! ⌫ + A⇤ interactions

with nuclei, and the Inverse Beta Decay (IBD). The latter is the most relevant forthe pre-Supernova neutrino detection.
The IBD is characterized by the reaction:⌫¯e + p ! e+ + n(1.11)

Neutrinos

52

Figure 1.7: Feynman diagram representing the Inverse Beta Decay interaction.

Equation (1.11) means that an anti-neutrino interacts with a proton, resulting ina positron and a neutron. The energy threshold of the interaction Ethr is:(me + mn )2
Ethr =
2mp

m2p= 1.806M eV,(1.12)

where me , mn and mp are the electron, neutron and proton masses, respectively.
A great part of the pre-Supernova flux is of electron anti-neutrino, so IBD
becomes the main detection channel for its detection.
The positron energy is related to the incoming anti-neutrino by:E⌫¯e = Ee+ + 1.293M eV,(1.13)

neglecting the recoil of the neutron. The direction of the incoming anti-neutrino

Neutrinos

53

can’t be determined from the outgoing direction of the positron at low energies,
as the positrons carry most of the kinetic energy of the anti-neutrino, but theneutron carries most of its momentum.
The overall IBD cross-section for energies less than 10 MeV is given by [44]:

=

where

rad and

0 (1 + rad )(1 +rad )(1 + rec+W M )(1 + thr ),(1.14)

rad are the ”Outer” and ”Inner” radiative corrections. The outercorrection is dependent on the positron spectrum and the inner correction is dependent on the hadronic structure.
weak magnetism andthr

rec+W M

represents the nucleon recoil andthe threshold correction. All these corrections havebeen calculated using di↵erent methods over the years, considering experimental
data [44; 45; 46]
The inverse beta decay is of extreme importance for the topic of this thesis.
Pre-Supernova electron anti-neutrinos interacting via IBD in Super-Kamiokande
will produce observable products by the detector. Descriptions of techniques for
identifying these products from equation (1.11) will be shown later.

Chapter 2

Supernova and Pre-Supernova

2.1Supernova Neutrinos

2.1.1

Supernova

A core-collapse Supernova occurs at the end of the life of a massive star, which
is a star with at least eight solar masses, when the core of the star goes througha gravitational collapse. This process releases an energy of 3 ⇥ 1053 ergs. About
99% of this explosion energy is released by neutrinos of all flavors with energies oftens of MeVs. Based on [47] the total rate of Galactic Supernovae to account theobservations in the last millennium is of approximately 4.6+7.4
2.7 per century.

54

Supernova and Pre-Supernova

55

Figure 2.1: Supernova Classification. Reproduced from [48].

After the explosion, this energy spreads through the universe, which is fundamental for the evolution of galaxies, planets, and other stars, making the Supernova phenomena a object of huge interest to cosmology.
Supernovas are classified in Types I and II with subcategories, as described in
Figure 2.1. The categorization is made by the abundance of specific spectral lines
and light curve. Type II Supernovae have H lines in their spectra while Type I lostits H envelope [49]. Based on [47], which considered modeled observability andhistorical records to estimate the Supernovae rates, Types Ia has a rate of 1.4+1.4
0.8
per century, while the other Types, which are core collapse Supernovae, have arate of 3.2+7.3
2.6 per century. There are many other estimations for Supernovae rates.
See e.g. [50], in which the rates were estimated by combining independent resultsfrom the literature.

Supernova and Pre-Supernova

56

Current models describe Type Ia as a Supernova due to thermonuclear disruption of white dwarfs. For stars with mass greater than 8M , where M

is theSolar Mass, the cause of the Supernova is the core collapse. They are known as
Types Ib, Ic, and II in the classification, and they are more interested in termsof neutrino physics because it releases all kinds of neutrinos, while Type Ia only
produce electron neutrinos by electron captures and much lower neutrino fluxes.
After the explosion, Supernovae can produce a compact remnant, a NeutronStar or a Black-Hole, depending on their original mass, composition and historyof mass loss. Usually, if the star had a mass between 8-20M , a very dense neutroncore is left, which is called Neutron Star. If the mass was greater than 30M , thestar collapse may go straight into a Black Hole, as failed Supernovae [51]. For a
Neutron Star, approximated by a homogeneous sphere with Newtonian gravity,
the binding energy, which is carried away by neutrinos, can be estimated as [52]:

3 GM 2E⇡⇡ 3.6 ⇥ 1053
5 R

✓

M
1.5M

◆2 ✓

R
10km

◆ 1

[erg],(2.1)

where M and R are the mass and radius of the star. This equation is consistent
with the observed for SN1987A, described in the following sections.
Next observation of a Supernova will provide much more knowledge about astrophysics, as well as Neutrino Physics and how great is the neutrino’s role forSupernova and Pre-Supernova

57

the explosion. Figure 2.2 shows core collapse Supernova candidates in a 1 kpc
distance.

2.1.2

Supernova Burst and Neutrino Emission

After all the burning stages of a star, i.e. nuclear fusion of hydrogen, carbon,
oxygen, neon and silicon, the star is left with an iron core of about 1 solar mass,
surrounded by shells of these elements with decreasing atomic mass. The central
density is about 1010 g/cm3 and temperature of about 1 MeV. This structure is sustained by the pressure of degenerated electrons. Processes of photo-disintegrationof iron 2.2 described in:+ 56 F e ! 13↵ + 4n

124.4M eV,(2.2)

will start to take place due to the high temperature and pressure, reducing the
kinetic energy of the electrons. Other processes will also reduce the pressure ofelectrons, for instance electron capture of nuclei 2.3:

e + N (Z, A) ! N (Z1, A) + ⌫e

(2.3)

The produced electron neutrinos leave the center carrying most of the kinetic
energy. These capture processes will last only a few microseconds until the pressure

Figure 2.2: Nearby core collapse Supernova candidates within 1 kpc. Colored labels show the star’s spectral Type.
Masses and distances of the stars are shown in parenthesis. From [53].

Supernova and Pre-Supernova
58

Supernova and Pre-Supernova

59

of the electrons can no longer sustain the structure and the collapse starts.
When the core reaches a super density of about 1011 -1012 g/cm3 , it can trap
neutrinos in the stellar matter. The inner part of the core at this stage collapses
with subsonic velocity proportional to the radius, while the outer part has supersonic free-fall velocity [48]. After one second of this instability, the inner corestops the collapse and finds equilibrium. A supersonic shock wave forms at its
surface, propagating to the outer core, which is still collapsing. The shock wave
will disassociate nuclei in the outer core, which will cause electron capture on free
protons, creating a large neutrino flux. However the flux will be trapped behindthe shock wave for its density. The shock breakout, when the neutrinos are released, happens when the shock wave reaches a zone with density of 1011 g/cm3 .
The correspondent neutrino emission is known as neutronization burst. It occurs
in the first ⇠25 milliseconds after the core bounce when the star emits with very
high luminosity mainly ⌫e neutrinos.
At this stage the inner core is still with high density and opaque to neutrinos.
The density needed for neutrino emission will be di↵erent for each flavor due todi↵erence in matter interactions. Neutrino-spheres will be created for di↵erent neutrino flavors, as they interact di↵erently with matter. The production of neutrinosin the inner core occurs because of di↵erent processes, such as electron-positronSupernova and Pre-Supernova

60

annihilation eq. (2.4):
e + e+ ! ⌫x + ⌫¯x(2.4)

electron–nucleon eq. (2.6) and nucleon-nucleon bremsstrahlung eq. (2.5):

N + N ! N + N + ⌫x + ⌫¯x ,(2.5)

e± + N ! e± + N + ⌫x + ⌫¯x ,(2.6)! ⌫x + ⌫¯x ,(2.7)+ e± ! e± + N + ⌫x + ⌫¯x ,(2.8)

plasmon decay eq. (2.7):

and photoannihilation eq. (2.8):

where x = e, µ, ⌧ .
These interactions in the dense core produces pairs of neutrino and anti-neutrinos
from all three flavours. It is produced around 3 ⇥ 1053 ergs of gravitational energy,
in which 99% goes into neutrinos, 0.01% into photons, and 1% into kinetic energy
ejecting material.
Due to the di↵erent interactions of the associated leptons, the evolution of theSupernova and Pre-Supernova

61

di↵erent flavours of neutrinos will be di↵erent, giving the following energy hierarchy:hE⌫e i < hE⌫¯e i < hE⌫x i ,(2.9)

where x = µ, ⌧ , and the energies range between 10 MeV and 30 MeV.
The increasing density of the core due to the presence of heavy elements will
make it reach a limit of 1.4M , known as the Chandrasekhar mass limit, startingthe core collapse Supernova within 10 seconds.
Thanks to optical measurements of living stars, we can have a very good estimation of the Supernova rate, e.g. [54; 55; 56; 57; 58]. However, many questions
about neutrino emission rates by Supernovae, energy spectrum and luminosity
behavior of each of the neutrino flavors are still to be answered (see review work[59] and references within).
It is clear the important role neutrinos have during the star explosion, fromburning stages before the Supernova to all of the di↵erent stages of accretion,
cooling, collapse and formation of compact remnants. The neutrino luminosity
from core-collapse Supernovae has, for a few milliseconds, a peak of ⇠ 1020 L⇠ 2.5 ⇥ 1053 erg/sec, where L is the solar luminosity, and average of ⇠ 1017 L ⇠1051 erg/sec during the star cooling process [60]. The understanding of Supernova
explosions is directly correlated to the understanding of neutrino physics.

Supernova and Pre-Supernova

2.1.3

62

SN1987A

The first observation of neutrinos produced outside of our solar system was thanks
to a Supernova explosion known as SN1987A in the Large Magellanic Cloud. It
was detected on February 23rd of 1987 due to an extremely strong neutrino burst.
It occurred 51.4 kpc from Earth, and the detection of the neutrino burst, withenergy of 1053 ergs, occurred three hours earlier than the optical telescopes andlasted about one minute.
SN1987A became our best laboratory to study Supernova neutrinos, but it is
the only Supernova in the history detected by a neutrino burst. It was observed bydi↵erent experiments at the time: Kamiokande II (11 events) [61], IMB (8 events)[62], Baksan (5 events) [63], and Mont Blanc Liquid Scintillator (5 events) [64]. A
comparison of data from Kamiokande and IMB for electron anti neutrino is shownin Figure 2.3.
The progenitor of SN1987A was a blue supergiant, contradicting old models
which predicted the Type II Supernovae to be produced only by red supergiants.
The remnant is believed to be a Neutron Star [49].
Neutrino observations from SN1987A helped to study properties of neutrinos,
axions, majorons, supersymmetric particles and unparticles. It also proved predictions regarding neutrino fluence from core collapse Supernovae, spectra, time

Supernova and Pre-Supernova

63

Figure 2.3: Comparison of the time integrated e↵ective anti-neutrino spectrum from SN1987A between data from Kamiokande and IMB. Reproduced
from [65].

profile, etc. [49], [66]. These features are very important for many predictions
regarding the Di↵use Supernova Neutrino Background, which is discussed in next.

2.2

Di↵use Supernova Neutrino Background

The Di↵use Supernova Neutrino Background (DSNB) is the overall neutrino fluxof all core collapse Supernovae that happened in the history of our universe.

Supernova and Pre-Supernova

64

These neutrinos are also known as Supernova Relic Neutrinos (SRN). In SuperKamiokande, thy have an expected event rate of 1.3-6.7 events/year/22.5kton,with energy between 10-30 MeV [67].
Equation 2.10 gives the di↵erential Supernova Relic Neutrinos flux in Earth,taking into account also redshift e↵ects.

d
=c
dE⌫

Z 1

RSN (z)(1 + z)

0dN⌫ [E⌫ (1 + z)] dt
dz,
dE⌫
dz

(2.10)

where RSN (z) is the Supernova rate at redshift z, E⌫ is the neutrino energy received, dN⌫ /dE⌫ is the number spectrum emitted by the explosion where the neutrino was emitted at a higher energy E⌫ (1 + z). The relation between cosmic timet and redshift z is given by dz=
dt

H0 (1 + z)p⌦⇤ + ⌦m (1 + z)3 , which depends

on the Hubble constant H0 and cosmological parameters ⌦.

Regarding the Supernova rate RSN (z), there are several models to predict its
value. After the information acquired from SN1987A discussed in the previous
section, it was possible get better estimations for it. Figure 2.4 shows the DSNB
spectrum for di↵erent models.
Recently, these models have been based on the history of cosmic star formation,
where the Supernova rate is proportional to the Star Formation Rate (SFR). The
SFR is a function of the star mass, which thanks to current measurements ofSupernova and Pre-Supernova

65

Figure 2.4: Predicted DSNB spectrum for di↵erent models and showing thethree main backgrounds for this measurement and the energy region where they
apply. Reproduced from [65].

emissions of living massive stars, gives a very reliable core collapse Supernova ratefunction.
These neutrinos have not been detected yet. However, detection capabilities
have been improved significantly in the last few years. It is predicted that the newphase of Super-Kamiokande, with gadolinium, will be able to detect a few of these
events per year [65].

Supernova and Pre-Supernova

2.3

66

Pre-Supernova Neutrinos

The amount of neutrino emission from a star increases significantly in early stagesof a Supernova. The Zero Age Main Sequence (ZAMS) of a star is when it stars
to burn hydrogen in its core through fusion reaction. After stars with ZAMS mass
> 8M

burn most of the Hydrogen, producing Helium, the density of its core

starts to increase, which makes the fusion of the available Helium into carbon.
After the ignition of the carbon burning, the star is classified as neutrino-cooledstar.
The terminology of neutrino-cooled star was introduced by David Arnett [68],and it is important since it distinguishes this very special stage of the star evolution, in which the main cooling mechanism of the star is the neutrino emission.
For instance, while the lifetime of stars can vary from million to billion years, theneutrino-cooled star lasts only hundreds of years. A neutrino-cooled star can reachneutrino luminosity of about 1012 L , while its photon luminosity is only 105 L[60].
When the neutrino-cooled stage begins, massive stars will usually proceed with
nuclear fusion of helium, carbon, oxygen, neon and silicon, getting a onion shapewith an iron core, characteristic structure of the star just before the core collapse,
represented in Figure 2.5. The burning of these elements is possible because ofSupernova and Pre-Supernova

67

Figure 2.5: Characteristic onion-shape of a massive star just before the corecollapse, where the shells show the abundance of each element. Reproduced
from [16].

very high temperatures of the star before the start of density limitation due toelectron degeneracy [16].
In temperatures above ⇠ 5 ⇥ 108 K, the electron-positron annihilation process
generating thermal neutrinos (eq. 2.11) becomes the star’s dominant form ofcooling. Other processes such as plasmon decay, neutrino brehmsstrahlung andpair production also generates thermal neutrinos at this stage. The cooling rateincreases with temperature, leading to fusion of even heavier nuclei [16].

Supernova and Pre-Supernova

68

e+ + e ! ⌫x + ⌫¯x ,(2.11)

where x = e, µ, ⌧ .
The last stage of these stars before the core-collapse is the Si-burning, which iscommonly known as pre-Supernova star. In other words, the pre-Supernova staris the massive star at the onset of the collapse. At this last stage, the star forms
an iron core and burns Silicon at a temperature of approximately 3.4 ⇥ 109 K.
Neutrinos emitted at the Si-burning stage have an average energy of 1.85 MeV.
Usually the definitions of neutrino-cooled star and pre-Supernova star are approximated into one, since the evolution after the carbon ignition is very fastcompared to the life of a star. Table 2.1 shows approximate duration of burning
stages for a M

star and the average energy of the emitted neutrinos for eachphase.
Table 2.1: Approximate duration of burning stages for a 20 M star and thefraction and average energy of electron neutrinos emitted by pair-annihilation[69].

Burning Stage

Duration⌫e (¯⌫e ) fraction

Average ⌫ energyC

300 years

42.5%

0.71 MeVNe

140 days

39.8%

0.99 MeVO

180 days

38.9%

1.13 MeVSi

2 days36.3%

1.85 MeV

Supernova and Pre-Supernova

69

Figure 2.6: Normalized spectrum (normalized energy distribution function)of the pair-annihilation neutrinos emitted during C (solid), Ne (dashed), O(dotted) and Si (dot-dashed) burning stage and the solar pp neutrinos (thinline), from [60]. The spectrum was calculated based on the Monte Carlo methodof [70].

Solar neutrinos and pre-Supernova neutrinos have similar energies. However,pre-Supernova stars emits anti-neutrinos of every flavor, which is not true for theSun. The neutrino luminosity during Si burning is 1013 times greater than thesolar neutrino luminosity. Due to their di↵erent spectrum and the presence of anti
neutrinos, the pre-Supernova stars can be detected from kiloparsec distances [71].
The spectrum of the pair-annihilation neutrinos and comparison with pp neutrinos
from the Sun is shown in Figure 2.6, but it does not include the produced antineutrinos, which is the main di↵erence between the two sources.

Supernova and Pre-Supernova

70

About 1/3 of the produced neutrino flux in pre-Supernova stars is of electron
anti-neutrino. This is of extremely importance for detection purposes, since its
detection can be done though the inverse beta-decay ⌫¯e + p ! n + e+ , alreadyintroduced in the previous chapter. This interaction for the pre-Supernova ⌫¯ehas a large averaged cross section ¯Si ' 0.7 ⇥ 10 43 cm2 , which is about threetimes more than the neutrino-electron elastic scattering [60]. Although the preSupernova neutrinos have lower energies than Supernova neutrinos, their emission
is over a very long timescale compared to the Supernova [16].
Stars with ZAMS mass < 8M do not have the Si-burning stage and have acore collapse made of O/Ne/Mg burning instead of an iron core collapse [48]. Verymassive stars > 30M may collapse directly to a black hole with no Supernova
explosion [51].

2.3.1

Pre-Supernova Models

As explained in the previous section, the main cooling mechanism of massive stars
at late stages of their evolution (C, Ne, O and Si burning) before core collapse
is by pair annihilation e+ e! ⌫¯⌫, producing all flavours of neutrino and anti-

neutrino pairs. Beta processes will also contribute significantly right before thecore collapse, with an average more energetic ⌫¯e [72]. Only electron anti-neutrinos
from the Silicon burning phase can be detected in Super-Kamiokande due to aSupernova and Pre-Supernova

71

larger cross section for detection of ⌫¯e through Inverse Beta Decay at their energy
range.
The rate in which pre-Supernova would happen is the same as the rate forSupernova for the progenitor star with ZAMS mass > 8M , if we consider that allthese massive stars will end as core-collapse Supernovae. There are many methods
that estimates this rate. In this work, the rate considered is of 3.2 per century[47].
In order to perform these estimations of the expected signal from pre-Supernovae
neutrinos in Super-Kamiokande, two models of the thermodynamics of stellar models were analyzed: Odrzywolek and Heger [71], [69] and Patton et al., [72]. Bothmodels provide online data sets for calculation of anti-neutrino emission during
the pre-Supernova phase of a star. Odrzywolek provides for stars with 15 M and
25 M [73] and Patton for 15 M and 30 M [72], which are used to estimate thepre-Supernova sensitivity in Super-Kamiokande.
The model from Odrzywolek and Heger assumes that the entire neutrino flux
comes from pair annihilation. The normalized spectrum for the emitted antineutrinos from Odrzywolek and Heger model is shown in Figure 2.7. For the
nuclear isotopic composition of the star, the model assumes a nuclear statisticalequilibrium (NSE), which is a treatment only dependent on the temperature, density and electron fraction, making it a simple flux estimate by only post-processing

Supernova and Pre-Supernova

72

Figure 2.7: For a 20 solar masses star, the emitted anti-neutrino spectrum
for di↵erent stages. Dashed line: carbon burning, dotted line: neon burning,short-dashed line: oxygen burning, and solid line: silicon burning. Reproduced
from [71].

an already existing stellar model.
The model from Patton et al., [72] includes a more complete evaluation of theneutrino flux from the pre-Supernova star, including contributions not only frompair annihilation, but also from plasmon decay, photoneutrino process,-decay,and electron capture. Patton et al. also uses a modern approach to the starevolution code, which couples the isotopic evolution to the stellar evolution, byusing the software MESA (Modules for Experiments in Stellar Astrophysics) [74].

Supernova and Pre-Supernova

73

Figure 2.8: IBD rate in Super-Kamiokande for the 12 hours before the corecollapse for di↵erent star evolution models. It is assumed a distance of 200
parsecs. Reproduced from [16].

The Inverse Beta Decay rate evolution in time prior to the core collapse in SuperKamiokande is illustrated in Figure 2.8 for the considered models, in a distanceof 200 parsecs. Figure 2.9 shows the evolution in time of neutrino luminosity andmean energy of emitted ⌫¯e .
Following [16], in order to calculate the expected signal from the considered
models, an adiabatic transition is assumed and the ratio of electron anti-neutrinos
is changed at high MSW resonance and depends on the mass ordering of neutrinos.
The assumed transition probability is P (¯⌫e ! ⌫¯e ) = 0.675 for normal mass ordering and P (¯⌫e ! ⌫¯e ) = 0.024 for inverted mass ordering. It accounts the change inSupernova and Pre-Supernova

Figure 2.9: Comparison of the considered models for the ⌫¯e luminosity andmean energy prior to the core-collapse. Reproduced from [16].

74Supernova and Pre-Supernova

75

ratio of electron flavour neutrinos due to the dense stellar medium and the e↵ects
of oscillations in vacuum.
Recently, Super-Kamiokande started a new period of observations with the addition of gadolinium sulfate to the water in the detector, which has good prospects
for detecting pre-Supernova neutrinos. As it will be explained in the next chapter, the enhanced neutron tagging by a more efficient capture of gamma-rays will
reduce backgrounds and allow the detection of ⌫¯e emitted during the Si-burning
phase. Although their mean energy is below the Inverse Beta Decay threshold, as
we can see in Figure 2.7, the tail could possibly be detected.
Figure 2.2 shows nearby stars within 1 kpc from Earth (which are listed also in
Appendix A.1). The best candidates for observation currently are ↵-Ori (Betelgeuse) and ↵-Sco (Antares) for their proximity. The expected signal from di↵erent
pre-Supernova models and characteristics of the star in Super-Kamiokande will beshown in Chapter 5.

Chapter 3

Super-Kamiokande withGadolinium

Super-Kamiokande is a 50 kton water Cherenkov detector located at the Kamioka
Observatory of the Institute for Cosmic Ray Research, University of Tokyo. It
was designed to study neutrino oscillations and carry out searches for the decay of
the nucleon operating since 1996, detecting neutrinos from natural and artificial
sources. It is a big collaboration between 40 institutions around the world. In
2020, Super-Kamiokande moved to its next phase, in which Gadolinium Sulfate
was dissolved to the water in the detector.

76Super-Kamiokande with Gadolinium

77

Figure 3.1: Schematic overview of the Super-Kamiokande Detector

3.1

Detector Overview

With a diameter of 39.3 m and height of 41.4 m, Super-Kamiokande is cylindricalstainless-steel tank filled with 50 ktons of water. It is located in the Kamioka minein Japan, overburden with 1000 m of rock (equivalent to 2700 meters water) to
reduce cosmic ray muon backgrounds. The reduced muon flux is by a factor of
105 . Figure 3.1 shows a schematic view of the Super-Kamiokande detector.
The detector is divided into an Inner Detector (ID) and an Outer Detector (OD).
This division is made by a cylindrical structure of 55 cm thickness, supportingSuper-Kamiokande with Gadolinium

78

PhotoMultipliers Tubes (PMTs) to face both the ID and the OD. The inner partis left with dimensions 33.8m diameter and 36.2m height and, around the innersurface, 26 sets of Helmholtz coils are placed to compensate the magnetic fieldfrom the Earth [5].
The OD has a thickness of about 2m and and it is composed of 1,185 8-inch
PMTs, facing the outside of the detector. It has a function of veto for charged
cosmic rays that generate background and shield for neutrons and gamma rays
emitted by radiation of the surrounding rocks. The ID had initially 11,146 20-inch
PMTs facing the inner part of the detector. The number of PMTs changed inother phases of the detector, as it will be explained in section 3.2. The PMTs
in the ID are responsible for the overall detection. The water mass in the ID
is of 32 kilotons. We also define the Fiducial Volume (FV) 2m inside the innerdetector, in order to reduce backgrounds, corresponding to 22.5 ktons of water,which contains approximately 7.5 ⇥ 1033 protons and 6.0 ⇥ 1033 neutrons. Thesebackgrounds come from radioactive impurities in the PMT glasses, as it will be
explained in Section 3.9. Figure 3.2 shows a illustration of the support structure
for the inner detector.
Each one of the modules showed in Figure 3.2 contains twelve 20-inch PMTs for
the ID and two 8-inch PMTs for the OD. The photo-coverage, which is coverageof the surface by the PMTs is about ⇠ 42%. The number of active PMTs insideSuper-Kamiokande with Gadolinium

79

Figure 3.2: Arrangement of PMTs in modules inside the Super-Kamiokande
Detector. From [5].

the detector varies with respect to the phase in which the experiment is running,which will be discussed in Section 3.2.
In order to enhance the ability to observe neutrinos and prepare the detector
for gadolinium sulfate loading, in 2018, it was conducted in Super-Kamiokande a
refurbishment work to the detector, which included water containment reinforcement, replacement of defective PMTs, and improvement of the piping system inthe tank. Figure 3.3 shows a picture taken inside the tank during the refurbishment work in 2018.

Figure 3.3: Picture taken during the refurbishment work in 2018 of the Super-Kamiokande inner detector. Copyright:
Kamioka Observatory, ICRR (Institute for Cosmic Ray Research), The University of Tokyo.

Super-Kamiokande with Gadolinium
80Super-Kamiokande with Gadolinium

81

A Monte Carlo simulation built on the GEANT3 framework called SKDETSIM
models the detector.

3.2

Super-Kamiokande Phases

The Super-Kamiokande experiment began to acquire data in April, 1996. Since
then, its operation has been divided into di↵erent phases:

SK-I : First phase of the experiment when it started taking data in April 1996
until July 2001, where it had to be shut down for maintenance. It was
operating with a photon coverage of 40% by 11,146 ID PMTs and 1,885 OD
PMTs.
SK-II : During the refilling of the tank in November 2001, an implosion of one
PMT destroyed more than half of the PMTs inside the tank. Then it started
acquiring data in October 2002 with only 5,182 ID PMTs, with a photon
coverage of 19%. Also to each PMT was attached a fiber reinforced plastic,
or FRP case to prevent accidents like before. It ran until October 2005,
when it stopped to be fully rebuild.
SK-III : After being rebuild, the experiment restarted acquiring data in July 2006
until September 2008 with now 11,129 PMTs in the ID and again a photon
coverage of 40%.

Super-Kamiokande with Gadolinium

82

SK-IV : The fourth phase of the experiment, which maintained the same numberof PMTs and photon coverage as before, but this time with the upgraded
Front-End Electronics. It operated between September 2008 until May 2018,
when a refurbishment started in the detector towards the new project of theexperiment known as SK-Gd.
SK-V : Started on January 29th , 2019. Phase in which the experiment started the
preparation for gadolinium sulfate loading. Some new PMTs that will be
used in the Hyper-Kamiokande experiment were installed for testing. It ran
until dissolving Gadolinium Sulfate in July, 2020.
SK-VI : The current phase of the experiment, started in July 14th , 2020. 0.02%
gadolinium sulfate was dissolved to the water in the detector to improve theneutron tagging efficiency.

3.3

Cherenkov Radiation

Since neutrinos do not have electric charge, they are very hard to detect. However,when they occasionally interacts with the water in Super-Kamiokande, charged
particles are generated and they can be detected through the Cherenkov radiation. When a charged particle moves faster than the phase velocity of light acertain medium, vparticle > c/n, where c is the speed of light in vacuum and n isSuper-Kamiokande with Gadolinium

83

the refraction index of the medium, it emits a Cherenkov light [75]. The threshold energy, which minimum energy to generate the Cherenkov light is given byequation 3.1.

Ethr = p1m(1/n( ))2

,

(3.1)

where m is the mass of the charged particle, n is the refraction index of themedium, andis the wavelength of the Cherenkov light. The threshold kinetic

energy for an electron or positron to emit Cherenkov light in pure water is 0.257
MeV.
The Cherenkov photons are emitted in a cone with an open angle:

cos✓C =

1
n( )

(3.2)

The number of photons emitted per unit length per unit wavelength is given byequation 3.3.

d2 N
2⇡z 2 ↵
=
2
dxd

✓

1

1
2 n2 ( )◆=2⇡z 2 ↵
2

sin2 ✓C ,where z is the particle charge in unit and ↵ is the fine structure constant.

(3.3)

Super-Kamiokande with Gadolinium

84

Figure 3.4: Spectrum of the Cherenkov radiation in water.

The Cherenkov light is emitted mostly in the UV range, but for Super-Kamiokande,
the typical range of photon wavelength detection by the PMTs is between 300nm
and 600nm. Figure 3.4 shows the Cherenkov radiation spectrum in water. Integrating over this range of wavelength:dN= 764z 2 sin2 ✓C photons/cm
dx

(3.4)

Super-Kamiokande with Gadolinium

85

Figure 3.5: Scheme of the Super-Kamiokande water system. Reproduced from
[5].

Also, the refraction index for pure water is approximately n ⇡ 1.34 and if we consider that we are dealing with ultra relativistic particles,⇡ 1, we have from equa-tion 3.2 that ✓C ⇡ 42 , so it is reasonable to assume that inside Super-Kamiokande,⇠342 Cherenkov photons are produced per centimeter. The Cherenkov photons
are detected by the PMTs, which are discussed next.

Super-Kamiokande with Gadolinium

3.4

86

Water Purification System

The water transparency a↵ects significantly the quality of data acquired by thedetector. Di↵erent impurities in the water need to be eliminated to maintain hightransparency. UV lamps are used to kill di↵erent bacteria, reverse osmosis andfilters for the removal of small particulates. Heavy ions are removed with an ion
exchange resin called Polisher Cartridge. Radon contamination is reduced withvacuum and membrane degasifiers. Figure 3.5 shows a scheme of the SK water
system that was used for pure water.
The water needs to be recirculated to maintain the transparency. In the ID, thewater injection is at the bottom and the extraction from the top. Also, the watertemperature needs to be low for better efficiency of PMTs and suppress growthof bacteria. Small increases in temperature at the top half of the ID causes adi↵erence in the water transparency between top and bottom of the detector. To
remove the e↵ects of the di↵erence in transparency, convections need to be done
during calibration works. Injected water is cooled below the tank temperature forthe convection.

Super-Kamiokande with Gadolinium

3.4.1

87

Preparation for SK-Gd

The current phase of Super-Kamiokande, SK-Gd (SK-VI), has gadolinium sulfate
loaded to the water in the detector. The compound was chosen for not absorbing much light in the range of Cherenkov photons and for being highly soluble,
inexpensive, and transparent in solution [78; 79]. Changes to the water system
had to be performed so the gadolinium sulfate would not be removed with otherimpurities from the water in Super-Kamiokande.
To study the impacts of gadolinium loading in Super-Kamiokande, a small detector called Evaluating Gadolinium’s Action on Detector System (EGADS) was
realized to simulate the injection of gadolinium sulfate to the water in SuperKamiokande and the water system. Figure 3.6 shows the apparatus of the water
system used in EGADS.
The water removed to the top of the tank goes through compatible with gadolinium sulfate UV lamps and filters to eliminate particulates and bacteria. To control
temperature, and consequently the solubility of gadolinium sulfate, chillers andheating exchangers are used. Fast-recirculation can also be applicable to procedures compatible with the gadolinium sulfate.
Anion exchange resins used are not the same as the ones used for the pure waterin Super-Kamiokande, but compatible with the gadolinium sulfate. Total organicFigure 3.6: Scheme of the EGADS water system to simulate the gadolinium sulfate injection in Super-Kamiokande.
AE = anion exchange resin, DI = dionisation, TOC = total organic coumpound lamp, RO = reserve osmosis. Reproduced from [79].

Super-Kamiokande with Gadolinium88Figure 3.7: Water transparency measurements of EGADS compared to SK-III/IV phases. Reproduced from [79].

Super-Kamiokande with Gadolinium
89Figure 3.8: Most recent water transparency measurements in EGADS, that from March 31st, 2018 started to simulate
the first phase of SK-Gd.

Super-Kamiokande with Gadolinium
90

Super-Kamiokande with Gadolinium

91

compound lamps are used to create free radicals with far-UV. A nanofilter can beused to separate the ions of gadolinium sulfate, so to simulate the removal of thecompound.
EGADS was able to run for years maintaining the water quality. Figure 3.7
shows the water quality for the loading of 0.2% gadolinium sulfate to EGADS
and Figure 3.8 shows it for the 0.02% loading, started in March 31st, 2018. Morediscussions regarding the EGADS detector will be held later in the chapter.

3.5

PhotoMultiplier Tubes

The PhotoMultiplier Tubes (PMTs) were developed by Hamamatsu Photonics
in collaboration with the Kamiokande collaboration for use in the Kamiokande
experiment. They were further improved for Super-Kamiokande for a better timing
resolution and discrepancy from dark current for a single photo-electron. Figure
3.9 shows a scheme of the 20-inch model.
The material used for the photocathode is bi-alkali (Sb-K-Cs), which is sensitive
between 300-600nm and has quantum efficiency peak around 390nm (Figure 3.10).
The PMTs used in the ID have a peak quantum efficiency of 21% at wavelengthsof 360-400 nm. The photoelectron collection efficiency is above 70%, with transit
time spread of 2.2 ns.

Super-Kamiokande with Gadolinium

Figure 3.9: General scheme of the 20-inch Photo-Multiplier. From [5].

Figure 3.10: Quantum Efficiency for 20-inch PMT. From [5].

92Super-Kamiokande with Gadolinium

93

After the accident in 2001, all the PMTs were covered with FRP (fiber reinforced
cases) and acrylic covers, as shown in Figure 3.11, which unfortunately increases
the dark noise.
Until SK-IV, the model of all PMTs used was the Hamamatsu R3600. It hasa dynode structure of 11 stage Venetian blind type. From the SK-V phase, it
is used both the PMTs model R3600 and also 140 Hamamatsu R12860 PMTs,
which were installed during the refurbishment work in 2018. These new models
will be installed on the future experiment Hyper-Kamiokande. They have a dynode
configuration of 10 stage box and line and, compared to the R3600 model, they
have twice the detection efficiency, twice as good charge resolution and more than
twice as good timing resolution [76]. Figure 3.12 shows one of the new PMT being
inspected before installation in Super-Kamiokande.
Each pulse created by each PMT in Super-Kamiokande will record information
regarding the pulse arrival time and total charge. If the collection of information
inside a time window by a number of PMTs is above some threshold, the system
is triggered and the data of hit time and charge are recorded. The total charged
collected is correlated with the energy of the charged particle that produced thelight. The time window is set to be the maximum time the light would take totravel the maximum diagonal distance inside the ID, which is approximately 200
ns [77]. Discussions of triggering conditions will be held in Section 3.7.

Figure 3.11: Picture taken during the full reconstruction of Super-Kamiokande in 2005, showing the PMT with
the Fiber Reinforced Case (FRC). Copyright: Kamioka Observatory, ICRR (Institute for Cosmic Ray Research), The
University of Tokyo.

Super-Kamiokande with Gadolinium
94Figure 3.12: Picture taken during the refurbishment work in 2018 of a new PMT being inspected installing inSuper-Kamiokande. Copyright: Kamioka Observatory, ICRR (Institute for Cosmic Ray Research), The University ofTokyo.

Super-Kamiokande with Gadolinium
95

Super-Kamiokande with Gadolinium

3.6

96

Electronics and DAQ

The DAQ electronics used in Super-Kamiokande changed over the years. From SKI to SK-III, the electronics were Analogue Timing Modules (ATM), in which timeto-analogue and charge-to-analogue converters were used [80]. Starting from SKIV, the ATM electronics were replaced with QTC-based Electronics with Ethernet(QBEE), with better charge and time resolution [81]. High speed charge-to-time
converters (QTC) and time-to-digital (TDC) are used. Each board has eight QTC
chips and each one of them takes the pulse produced by each PMT. The gainingof the QTC is optimized to give improved dynamic ranges and resolutions. The
TDCs tracks the QTC output signal and sends the starting time and signal width
to the data sort mapping FPGA, which saves into 6 bytes the source channel,
charge and start time. This data is stored by another FPGA to the first-in-firstout (FIFO) memory. Data from the FIFO memory to the front-end computers
are transferred by an Ethernet interface card as TCP packets. Data will be thensorted in 20 ms blocks.
Parallel computers merge the data blocks into time order and build the hit data
into events. Then an organizer computer sorts the data blocks, resulting in asingle data stream of events sorted in time, which are then saved to the disk [82].
Triggering is handled by software, which allows changes to trigger conditions.

Super-Kamiokande with Gadolinium

3.7

97

Trigger Conditions and WIT

A significant proportion of hits inside the DAQ time window is due to dark noise,
which is caused by thermionic emission of electrons from the photocathode and due
to radioactive decays in the PMT material [83]. Events with very low numbers ofhits are dominated by these backgrounds, so applying thresholds is a way to reducethem. Di↵erent triggers are applied in Super-Kamiokande, which are related tothe energy region of neutrino being analyzed. Table 3.1 shows the di↵erent triggers
and their conditions.
Table 3.1: Trigger schemes in Super-Kamiokande.

Trigger

Condition

Rate

Wide-band Intelligent

WIT

-

⇠ kHz

Super Low Energy

SLE> 34/31 hits

⇠ kHz

Low EnergyLE> 47 hits⇠ 40 kHzHigh EnergyHE> 50 hits⇠ 10 kHzSuper High Energy

SHE> 70/58 hits

After

AFT

(SHE).(OD)

Outer Detector

OD> 22 hits in OD

The Wide-band Intelligent Trigger (WIT), as seen in table 3.1, does not apply
any threshold to the minimum number of hits recorded by the PMTs. The WIT

Super-Kamiokande with Gadolinium

98

system is installed and integrated in the online DAQ system in Super-Kamiokande,
and it was design to process all the data, extracting and reconstructing very low
energy electrons with efficiency close to 100% in real time, with the use a greatamount of parallel computing [83]. The system discards events with reconstructedvertices close to the PMTs or not well reconstructed, in this way eliminating manybackground events due to radioisotope decays in the PMT glass and FRP cases,
for their closeness to the PMTs.
The WIT system is composed of seven computers that receive, triggers and reconstructs raw data blocks. These data blocks contain time and charge informationof 1344 consecutive data reads (which is about 23 ms of data). In a 220 ns time
window, which is the maximum time Cherenkov photons can take to transverse thedetector as explained in Section 3.5, coincidence signals of 11 hits (around 2 MeV)
above the dark noise (usually 12 hits) are searched inside the data blocks. Hits
passing this criterion are called ”pre-triggered events” and to them is applied vertex reconstruction, described in the next Section 3.7.1. The reconstructed events
are sent to an eighth computer, which sorts the events by time. The sorted events
are then transferred to the o✏ine disk for physical analyses [83].

Super-Kamiokande with Gadolinium

3.7.1

99

Event Reconstruction

The first filter applied to pre-triggered events is the Software Trigger Online Reconstruction of Events (STORE). The filter looks for PMT hits that might beoriginating from a single vertex, since low energy electrons can only travel a few
centimeters in water, so their Cherenkov light is approximately a point source [83].
The filter calculates the temporal and spatial separations among hits. The largest
set of hits that are consistent with being from a point source is selected.
The vertex fit ClusFit is applied to the hits selected by STORE, in which isolated
hits are eliminated to reduce e↵ects of reflected and scattered light and dark noise.
If the vertex reconstructed by ClusFit is inside the Fiducial Volume, the Branch
Optimization Navigating Successive Annealing Iterations (BONSAI) fitter will beapplied.
BONSAI performs a maximum likelihood fit to timing residuals of the PMT
hits, testing each vertex hypothesis. Timing residual is defined as:

Tresidual = Thit

Tf light

Tvertex ,(3.5)

where Thit is the PMT hit time, Tf light is the time of flight from the testing vertex
to the hit PMT, and Tvertex is the time of the testing vertex. The hypothesis withmaximized likelihood is chosen as reconstructed vertex.

Super-Kamiokande with Gadolinium

100

Figure 3.13: Likelihood values as a function of the total electron energy andopening angle between the reconstructed direction and the direction from vertex
to each PMT. From [85].

BONSAI is a much more accurate fitter, but slower. For this matter, applying
STORE and ClusFit, which are fast filters, is important for CPU time considerations [84]. BONSAI returns also a goodness value to evaluate the quality of theapplied fit.
For the direction reconstruction, an energy dependent likelihood is used to compare Cherenkov ring patterns between data and MC simulation. These patterns
and their energy dependences were simulated for di↵erent energy ranges [85]. Figure 3.13 shows the likelihood as a function of electron energy and open angle
between the reconstructed direction and the direction from vertex to each PMT.
The likelihood of hits in an event is maximised to get the most likely direction.

Super-Kamiokande with Gadolinium

101

Then it is applied, around the reconstructed direction, a Kolmogorov-Smirnov(KS) circular test on the azimuthal symmetry of the Cherenkov cone, resulting
in a ”dirks” score, which lower values can indicate a mis-reconstructed vertex.
Many backgrounds in Super-Kamiokande come from radioactive decays in the
PMT glasses. These events can be rejected for having low dirks score, since they
are not well fit due to their proximity to the walls.
For the energy reconstruction, the calculation is done using the number of PMT
hits with a residual time within 50 ns window, called N50 . However, before converting this number of PMT hits to energy, many corrections are applied to take
into account e↵ects of scattering, reflection, water transparency and dark noise, soan e↵ective number of hits can be obtained, which is called Nef f , given by equation
3.6.
N50 ⇢
X
Nef f =
(Xi
i=1

✏tail

✏dark ) ⇥ri
RcoverNall⇥⇥ e (run) ⇥ Gi (t) ,Nnorm S(✓i , i )(3.6)

where the factors of these equations correspond to: occupancy (Xi ), late hits(✏tail ), dark noise (✏dark ), bad PMTs (Nall and Nnorm ), photo-cathode coverage(Rcover and S(✓i ,i )), water transparency (ri and(run)) and PMT gain (Gi (t)).

The total energy is calculated as a function of Nef f . The energy conversion isprecisely calibrated using LINAC data, to be discussed in Section 3.8, and theSuper-Kamiokande with Gadolinium

102

Figure 3.14: Energy resolution as a function of energy found using Monte
Carlo events in [77].

accuracy the the absolute energy scale is better than 1% [86]. Figure 3.14 showsthe energy resolution as a function of energy.
Other important variables calculated by the event reconstruction are: n18,
which is the number of hits in the interval

6 ns < Tresidual < 18 ns at thereconstructed vertex, f romwall, which is the distance from the reconstructed vertex to the wall of the detector by the shortest vector, and ef f wall, which is thesame distance as f romwall, but along a vector defined by the reconstructed direction. All these variables are important later when describing the event selection
to study the sensitivity for pre-Supernova at Super-Kamiokande.

Super-Kamiokande with Gadolinium

3.7.2

103

Isotropy Parameters

Other variables can be calculated after the vertex reconstruction. For instance,
the angular distribution of PMT hits can characterize an event by its isotropy.
The parametersl , given by equation 3.7, represents a good way to quantify theisotropy of an event [87].

2
l =
N (N

N
N
X1 X

1) i=1 j=i+1

Pl (cos✓ij )(3.7)

This equation considers that a vector is traced from the reconstructed vertex toeach PMT hit, and the angle between pairs of vectors i and j is ✓ij . The isotropic
parameter is the average of the Legendre polynomial of cos(✓ij ). Higher values ofl indicates less isotropy [16].

In Thermal Neutron Capture (TNC) on gadolinium, multiple -rays with random directions relative to each other will be created, but the pattern of PMT
hits in Super-Kamiokande should be isotropic. So, discriminating events by theirisotropy can be contribute to reduce backgrounds.

Super-Kamiokande with Gadolinium

3.8

104

Calibrations in Super-Kamiokande

The calibration procedures in Super-Kamiokande have great importance for thephysics analyses and event reconstruction done in Super-Kamiokande. The PMT
and electronics responses, water properties and other items are calibrated to be
used as parameters for the Monte Carlo simulation of the detector, SKDETSIM,
so it can be used with confidence in the data analyses.
For the calibration of the Super-Kamiokande for low energy neutrino measurements, a linear accelerator (LINAC) for electrons installed in the detector is used.
The electrons are injected into the ID with a known energy. LINAC data is taken
at various positions in the detector, getting detector responses to the variables
relevant to low energy analyses. Electrons can be selected between 5-16 MeV/c[88].
LINAC is located near the top platform of the tank and the beam goes throughan evacuated pipe and guided by magnets along the platform. The pipe is extended
to di↵erent positions in the tank, as illustrated in Figure 3.15.
The calibration with LINAC is used to set absolute energy scale for low energy
analyses and also for determining energy, vertex, and angular resolutions. Other
calibration techniques are used to calibrate absolute energy scale, for example

Super-Kamiokande with Gadolinium

105

Figure 3.15: Scheme of LINAC calibration at the SK detector. The dashedline represents the fiducial volume. The black dots indicate where the calibration
data is usually taken. From [88].

the DT (deuterium-tritium) neutron generator [89]. More information regarding
calibrations in Super-Kamiokande can be found in [90] and [88].

3.9Super-Kamiokande with Gadolinium

From July 14th , 2020, Super-Kamiokande officially started its new phase, SK-VI,
in which 0.02% of gadolinium sulfate, Gd2 (SO4 )3 ·8H2 O, by mass was added to thewater in the detector. It is usually referred as Super-Kamiokande with Gadolinium,Super-Kamiokande with Gadolinium

106

SK-Gd. The project was proposed in 2003 [78], with the main motivation is thedetection of DSNB. The final concentration goal of Gd2 (SO4 )3 · 8H2 O is 0.2% bymass.
The detection of DSNB is through Inverse Beta Decay (IBD) in Super-Kamiokande.
The products of IBD are a positron and a neutron. The Thermal Neutron Capture(TNC) on Hydrogen emits 2.2 MeV -rays, which has low detection efficiency forbeing close or below the Cherenkov threshold. The estimated detection efficiency
for neutron captures on Hydrogen is approximately 20%, assuming that the -rays
are uniformly produced in the detector [91]. With small amounts of Gadolinium(Gd) dissolved in the water of the detector, the majority of TNC will be on it,
due to its high neutron capture cross section [16]. While the TNC cross section
in hydrogen is only 0.3 barns, the average e↵ective cross section in gadolinium ishigh as 49,000 barns. The largest contributions for the neutron capture come fromthe isotopes 157 Gd and 155 Gd. Table 3.2 shows the properties of these isotopes,
comparing to the capture on Hydrogen.
Table 3.2: Properties of Gd isotopes, in comparison with Hydrogen. From theinformation in [79].

IsotopeNatural Abundance

TNC cross section

Energy157

Gd15.65%

255,000 barns

7.9 MeV from -ray cascade

155

Gd

14.80%

61,000 barns

8.5 MeV from -ray cascade

0.3 barn

2.2 MeV from single -rayH

Super-Kamiokande with Gadolinium

107

The -ray cascade produced by the TNC on Gd has a higher energy than on
Hydrogen, so many more photons of Cherenkov photons are detected. The energyof the cascade is around 8 MeV. This will allow the detection of DSNB withina few years of SK-Gd operation [78]. Other analyses such as galactic supernova
neutrinos, atmospheric neutrinos, proton decay, reactor neutrinos will benefit fromthe Gd loading.
The current phase of SK-Gd corresponds to the loading of 0.02% gadolinium
sulfate by mass (or 0.01% Gd), causing approximately 50% of TNC to be on
Gd. The final goal is to have Super-Kamiokande loaded with 0.2% gadolinium
sulfate by mass (or 0.1% Gd), which will cause approximately 90% of TNC on Gd.
The -rays from TNC produce signals in Super-Kamiokande mainly by Compton
scattering electrons, which produce Cherenkov photons. Figure 3.16 shows theneutron capture efficiency for three planned phases of SK-Gd, with concentrationsof gadolinium sulfate of 0.02%, 0.06% and 0.2%.

3.9.1

EGADS

To simulate the gadolinium sulfate loading in Super-Kamiokande, the detector
EGADS (Evaluating Gadolinium’s Action on Detector System) has been used.
Features regarding the its water system were introduced in section 3.4. EGADS
was designed to be a small prototype of SK-Gd. It is a water Cherenkov detector

Super-Kamiokande with Gadolinium

108

Figure 3.16: Neutron capture efficiency for di↵erent gadolinium sulfate concentrations in Super-Kamiokande. Edited from [92].

with 20-inch PMTs, but with only 200 tons of water and without an outer detector.
Figure 3.17 shows a schematic view of the detector.
EGADS has 227 PMTs installed, in which 148 have no cover, 16 have an
FRP housing only, and 60 have both an FRP and acrylic cover like in SuperKamiokande. It has the same photocathode coverage of 40% and a similar coil tocompensate the Earth’s magnetic field. It uses the same QBEEs for data acquisition [79].
EGADS was able to maintain the water quality without loss of gadolinium. The
initial gadolinium sulfate loading reached the concentration of 0.2% by mass andran for a few years. Tests for the 0.02% gadolinium sulfate loading and operation
ran from March 31st , 2018 until November 18th , 2020, when it started the tests forFigure 3.17: Scheme of EGADS detector. On the left a side view of the tank and on the right the floor and the PMT
support frame. Reproduced from [93].

Super-Kamiokande with Gadolinium
109Super-Kamiokande with Gadolinium

110

the next phase of SK-Gd, in which the concentration of gadolinium sulfate will be
0.06% by mass.
Besides being a prototype for SK-Gd, EGADS is used also as a neutrino detector.
It has its own galactic core-collape Supernova alert system called HEIMDALL(High Efficiency IBD Monitoring Detector and Automated caLL) [94].

3.9.2Radiopurity

Many of the backgrounds in Super-Kamiokande come from radioactive decays fromthe impurities distributed in the water of the detector or in the PMT glass andcovers. For that reason, it is important to consider a fiducial volume to eliminate
events due to radioactive decays near the walls of the detector.
Radon contamination, as already explained in previous sections, are partiallyreduced by temperature control and fiducial volume cuts.
The addition of gadolinium sulfate to the water in Super-Kamiokande will bringcontaminations that are uniformly distributed through the detector. Backgrounds
come from the production of -rays or -rays from uranium U (Figure 3.18), thorium T h (Figure 3.19), and actinium Ac (Figure 3.20) chain isotopes. Acceptable

Super-Kamiokande with Gadolinium

Figure 3.18: Uranium 238 decay chain, from [95].

111Super-Kamiokande with Gadolinium

Figure 3.19: Thorium 232 decay chain, from [95].

112Super-Kamiokande with Gadolinium

Figure 3.20: Actinium 227/Uranium 235 decay chain, from [96].

113Super-Kamiokande with Gadolinium

114

Figure 3.21: Scheme of the gadolinium sulfate dissolving plan to the SuperKamiokande detector. From [92].

levels of contamination from these radioisotopes were determined by the low energy neutrino analyses, which established upper limits that would be tolerable forthe DSNB and solar neutrino physics goals [79].

3.9.3First Gadolinium Loading and Future

Approximately 13 tons of gadolinium sulfate Gd2 (SO4 )3 · 8H2 O were dissolved
between July 14th and August 18th in 2020. This loading was to achieve a concentration of 0.02% by mass. Figure 3.21 shows a scheme of the plan for dissolving:
gadolinium loaded water is inject to the bottom of the tank.
After the start of injection of the compound to the tank, samples of water weretaken in four di↵erent positions in Super-Kamiokande: two in the ID and twoin the OD. Temperature and conductivity were measured to trace the spread ofgadolinium in the detector. With the water samples it was possible to measure
the concentrations at each step of the loading program.

Super-Kamiokande with Gadolinium

115

After the loading was finished, the circulation flow of the water system was set
to 120 m3 /hour, starting the convection mode to get a uniform concentration ofgadolinium in the tank. Water quality improved gradually, but the dark rate of all
PMTs increased significantly during this phase. Figure 3.22 shows the evolutionof the dark rate. The main hypothesis for the increased dark rate is the presenceof bacteria in the ID and studies about the phenomenon is being currently held.
Dark rates decreased after changing the water flow and dropping the temperatureby 0.05 C.
At the time of writing, dark rates are still decreasing and the water quality is now
similar to previous Super-Kamiokande phases. EGADS is currently simulating thenext phase of SK-Gd, in which the concentration of 0.06% Gd2 (SO4 )3 · 8H2 O bymass will be reached in the detector. The next loading is planned for 2022. The
final goal of SK-Gd is to have the concentration of 0.2% Gd2 (SO4 )3 · 8H2 O by
mass.

3.9.4

Simulation of TNC on gadolinium

The evaluation of the pre-Supernova sensitivity at SK-Gd uses simulation of TNC
on gadolinium in Super-Kamiokande. The current detector simulation, SKDETSIM does not include the TNC on gadolinium. To better simulate SK-Gd, a new

Figure 3.22: Evolution of the water transparency in the Super-Kamiokande detector and of the PMTs dark rates forthe end of SK-V and first months of SK-VI (after gadolinium loading).

Super-Kamiokande with Gadolinium
116Super-Kamiokande with Gadolinium

117

Figure 3.23: Emission energies in the two considered models for TNC on
gadolinium. From [16].

detector simulator based on GEANT4 [97], which will include the neutron capture
on gadolinium, is in development.
Following what it was done by [16], -rays vectors are generated externally to
SKDETSIM. Two di↵erent -ray emission models are used for that, the genericliquid scintillator simulator GLG4SIM, developed by the Chooz experiment [98]and a second model created at the Japan Proton Accelerator Research Complex(J-PARC) after measurements of -ray properties with an array of germanium detectors in the Accurate Neutron-Nucleus Reaction Measurement Instrument (ANNRI) at the facility [99].

Super-Kamiokande with Gadolinium

118

Figure 3.24: Multiplicity of -rays in the two considered models for TNC on
gadolinium. From [16]The spectrum of -rays produced by TNC on gadolinium consists of a few known
energy -rays close to the Q value, and a continuum at lower energies where the levels are so densely populated that they are indistinguishable [100]. The GLG4SIM
model combines a parametric model of the continuum with the known physics ofhigh energy -rays. The model provided by J-PARC includes its measurementswith ANNRI to account -ray combinations that come together, and also a different continuum distribution. Both models assume a isotropic distribution fromthe angles of -ray emission.
The energy distribution and multiplicity of both methods are compared in Figures 3.23 and 3.24. Some reconstructed variables in both models are shown in
Figure 3.25. The two models are included in this work to provide systematic
uncertainties for the pre-SN neutrino sensitivity results in Super-Kamiokande.

Super-Kamiokande with Gadolinium

Figure 3.25: Comparison of di↵erent variables for the two models of TNC on
gadolinium in consideration. From [16].

119Chapter 4

Low Energy Inverse Beta Decay
Detection in Super-Kamiokande

As explained before, the detection of low energy electron anti-neutrinos from preSupernova stars in Super-Kamiokande is through the Inverse Beta Decay (IBD)
interaction:⌫¯e + p ! e+ + n(4.1)

Positrons are detected by their produced Cherenkov light, explained in Section
3.3. Neutrons are thermalised and captured by free protons and in SK-Gd also by
gadolinium. They travel a short distance before being captured, meaning that an
IBD event is represented by a prompt part, which corresponds to the reconstructed120

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

121

positron, and a delayed part, which is the reconstructed neutron. The time between these two parts of the IBD event is called coincidence time and the distance
between them is called coincidence distance. The IBD events are also called Coincidence Events. This signature is used to distinguish background events, since
the probability of uncorrelated events to produce this signature is low.
The goal of this work is the implementation of a Supernova alarm based on thedetection of pre-Supernova neutrinos starting from the first phase of Gd loading inSuper-Kamiokande, which is 0.02% Gd2 (SO4 )3 · 8H2 O by mass. For that, methods
are optimized for the current phase and they will be eventually improved for next
phases of SK-Gd.
In this chapter details on reconstruction of IBD events is described, as well as
discussing the main backgrounds for the pre-Supernova neutrinos detection andthe techniques used to reduce them.

4.1

Inverse Beta Decay Identification

One of the challenges for the detection of pre-Supernova ⌫¯e in Super-Kamiokande
is that the positron produced by the IBD interaction has a very low energy, since
the expected ⌫¯e energy from pre-Supernova is also low. The total energy of theLow Energy Inverse Beta Decay Detection in Super-Kamiokande

122

IBD positron is related to the ⌫¯e energy:

E e+ = E ⌫

where

= mn

,

(4.2)

mp = 1.293 MeV [100]. If not enough photons are detected, these

events might not get reconstructed. Figure 4.1 shows the trigger efficiency of WIT,
which is 100% for electrons with total energy over 4 MeV, but drops significantly
below this energy. This trigger efficiency was evaluated with simulation and thefigure shows true energy, not reconstructed energy [100]. The simulations were
performed with SK-IV conditions and the same trigger conditions are expected
for SK-Gd.
The detection of the neutron produced in IBD interaction, equation 4.1, occurs
by the thermal capturing of the particle by free protons or gadolinium. The TNC
on gadolinium emits an approximately 8 MeV -ray cascade, while for hydrogen
this cascade has only 2.2 MeV. Neutrons travel a shorter distance before being captured by gadolinium compared to Hydrogen, and the shorter coincidence distance
improve the vertex reconstruction resolutions and detection efficiency, lowering
backgrounds and increasing tagging efficiency. The addition of gadolinium sulfate
lowers the coincidence time from 220 µs to about 120 µs for the 0.02% gadolinium
sulfate loading.
Using the coincidence detection of neutron and positron to identify the IBD

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

123

Figure 4.1: WIT efficiency compared to electron true total energy for eventsin the whole ID and only inside the FV. The Cherenkov threshold and SK-IV
solar neutrino analysis threshold are showed for comparison.

interaction, increases the tagging efficiency, improving the capability in distinguishing signal from background. These two parts of the event are handled independently by WIT, as a coincidence trigger, which will characterize the detectionof both positron and neutron as the same event, is still in development for the
WIT system.
Two detection channels are considered for the pre-Supernova detection. If bothproducts of the IBD reaction are detected, with prompt and delayed parts, i.e.
observation of coincidence events, it is called Delayed Current (DC) channel. If
only TNC events are observed, it is called Single Neutron channel.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

124

The detection of the electron anti-neutrinos from a pre-Supernova will, apartfrom find properties of the massive stars in the stages preceding a Supernova andinitial conditions of the core-collapse, give warnings to di↵erent experiments inorder to prepare for the observation of the subsequent Supernova a few hours inadvance.

4.2

Backgrounds

The sources of background for the pre-Supernova neutrino are di↵erent for the two
considered channels for the analysis, single neutrons events and delayed current(DC) events. The evaluation of backgrounds was done in two parts: initially it
was reproduced the rates previously obtained in [16] and later a re-evaluation of
the background sources with new considerations needed for current status of thedetector. The background studies have been also performed with an optimized
selection, which will be described in Section 4.3.
In this section, it will be shown the sources of background and the di↵erences
between the previous and current estimations.
The main backgrounds for the single neutron channel include:• Fake neutrons, from radioactive decays and dark noise.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

125• Real neutrons that are not from pre-Supernova neutrinos.
• Reactor neutrinos and geoneutrinos.
And for the DC channel:• Accidental coincidences, from radioactive decays and dark noise.
• Spontaneous Fission of 238 U, due to radioisotope contamination in the detector.
• Pairs of neutrons, also due to radioisotope contaminations.
• Spallation, due to cosmic rays muons.
• Reactor neutrinos and geoneutrinos.
The evaluated backgrounds for the pre-Supernova analysis will be shown in
Chapter 5. A more detailed description of these sources is given next.

4.2.1

Fake Neutrons and Accidental Coincidences

Fake neutrons and accidental coincidences are intrinsic backgrounds for the single
neutron and DC channels respectively. They come from radioactive decays anddark noise. Previous estimations of these backgrounds were done in [16] by using

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

126

Figure 4.2: Comparison of expected accidental coincidences events over timefor SK-IV and SK-V pure water data. For SK-V, results with improved selection
techniques are shown. These techniques will be discussed in Section 4.4.3.

6000 hours of SK-IV real pure water data. The same background rates wereobtained in this work for the same conditions.
Since these backgrounds come mainly from dark rates, which have changed since
SK-IV, a re-evaluation of these background rates were performed using approximately 1000 hours of SK-V pure water data. Figure 4.2 shows the comparison of
accidental coincidences rates for SK-IV and SK-V. At the time of writing, SK-VI
is still in commissioning phase, so evaluations with gadolinium data will be donein the future.
Other intrinsic backgrounds due to physical characteristics of the detector areLow Energy Inverse Beta Decay Detection in Super-Kamiokande

127

expected. The materials inside the detector can emanate radon and also haveradioactive decays. These e↵ects happen close to the detector walls, so FV cuts
generally reduce them.

4.2.2

Radioactive Contamination

With the loading of gadolinium sulfate to the water in Super-Kamiokande, radioactive impurities are distributed in the detector. In order to avoid undesirable effects in the physics analyses of Super-Kamiokande, these contamination have been
carefully quantified and studied continuously before and after the first loading, bymeasuring them with HPGe detectors and collaborating with manufacturers tomatch acceptable levels of contamination [79].
Requirements for maximum levels of contamination of some radioisotopes havebeen defined by the Solar Neutrino and the Supernova Relic Neutrino (SRN)
analyses and are shown in table 4.1.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

128

Table 4.1: Radioisotopes activity level required for the Supernova Relic Neutrino and Solar Neutrino analyses.

Requirements (mBq/kg)
Chain
238

232

235

Part of the chain

SRN

Solar ⌫

238

U

<5

-

226

Ra

-

< 0.5

228

Ra

-

< 0.05228

Th-< 0.05U-< 30.0Ac/227 Th-< 30.0

U

Th

235

U
227

The presence of these radioisotopes in the gadolinium sulfate will be responsiblefor di↵erent sources of background for the pre-Supernova analysis: real neutrons,
pairs of neutrons and spontaneous fission.
238

U will contribute primarily to the spontaneous fission background, process inwhich one or more neutrons are produced, leading to real neutron background tosingle neutron events, as well as backgrounds for DC events with the coincidenceof a -ray with one of these neutrons or two neutron detection as delayed event.
Di↵erent multiplicities need to be accounted when evaluating these backgroundrates [101], depending on which event type, single or coincidence, is being analyzed.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande
235

129

U on the other hand will contribute mainly to the pairs of neutrons back-ground. This isotope and its chain are ↵ emitters, increasing the rates of 18 O(↵,n)21 Ne⇤
processes. Each reaction of 21 Ne⇤ decay produces two neutrons, so one of the neutrons could be mistaken for a positron, becoming a background for coincidence
events.
For the background rates estimations it was considered the usual isotope concentrations in Nature of 0.20% for 18 O and 0.038% for 17 O. Neutrons will comemainly from 18 O and the main ↵ sources are 211 Po and 215 Po, coming from theAc/235 U decay chain [16].
Using the software SOURCES-4C [102], the rates of these backgrounds for both
0.2% and 0.02% gadolinium sulfate concentration were calculated for the current
activity level requirements and are shown in Table (4.2). These results are also
compared previous background predictions made by [100] based on di↵erent requirements for the activity levels from the radioisotope contaminations.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

130

Table 4.2: Rates for backgrounds per hour due to radioisotopes contamination
on gadolinium sulfate. Requirements for the current analysis are shown in Table
4.1. Previous requirements for contamination di↵er from current ones for 235 U< 3mBq/kg.

Pairs of Neutrons

Spontaneous Fission

Reported in [100], for 0.2% Gd2 (SO4 )3 · 8H2 O

0.28

0.62

Previous requirements, for 0.2% Gd2 (SO4 )3 · 8H2 O

0.275

0.624

Current requirements, for 0.2% Gd2 (SO4 )3 · 8H2 O

2.298

0.624

Current requirements, for 0.02% Gd2 (SO4 )3 · 8H2 O

0.231

0.062

4.2.3

Reactor Neutrinos

One of the main sources of backgrounds for pre-Supernova neutrino detection is
Reactor Neutrinos due to active Japanese nuclear reactors. They are in the sameenergy range and are detected on Super-Kamiokande through the same interaction,
so it is an irreducible background.
The website geoneutrinos.org is used to evaluate reactor neutrinos and geoneutrinos fluxes and the rate and energy spectrum of anti-neutrino interactions inSuper-Kamiokande [103]. The accounting of reactor activity, the mean values
given by IAEA’s PRIS for specific years of activity are used [104].

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

Figure 4.3: Comparison between reactor fluxes for the di↵erent years considered to estimate the background due to reactor neutrinos.

Figure 4.4: Background rates for the di↵erent years in consideration. Resultswith improved techniques are shown. These techniques will be discussed in
Section 4.4.3.

131

Low Energy Inverse Beta Decay Detection in Super-Kamiokande132

In [100] the fluxes of years 2010 and 2017 are considered to define the highest
and lowest expected backgrounds. In 2010 most of the Japanese nuclear reactors
were active, while 2017 represented the period with less reactor activity.
For the application in the pre-Supernova alarm, as well as to give more current
estimations for the reactor and geoneutrinos background, the most up-to-date
information from geoneutrinos.org was used in this work. So far, the most recent
values provided are for the year 2019. Figure 4.3 shows a comparison of reactor
fluxes for the years of 2010, 2017, 2018, and 2019. Figure 4.4 shows the evolutionof expected reactor neutrinos and geoneutrinos background rates.

4.2.4

Spallation

One of the main sources of background for low energy analysis in Super-Kamiokande,such as Solar neutrino and DSNB, is the nuclear spallation reactions, which are
initiated by beta decays of isotopes produced by cosmic ray muons and their produced daughter particles [105]. Both the cosmic ray muons and the daughter
particles pass through the detector.
Neutrons produced by the cosmic rays muons are not a concern. If these fastneutrons are produced before the cosmic ray muon reaches the detector, they would
need to pass over 4.5 m of water to be captured, so they mostly do not penetrate the
FV. With correlations between low energy events and muon tracking, the products

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

133

Figure 4.5: Distribution of capture times for the fast neutrons and for neutrons
from spallation daughters. From [16].

of spallation are well rejected by Super-Kamiokande by removing events associatedwithin a time and space windows with the muon track [106]. The backgroundfrom fast neutrons are controlled by vetoing for 1 ms after each muon, as they arethermalised and captured very quickly [100]. Figure 4.5 shows the distribution of
capture times for the fast neutrons and for neutrons from spallation daughters.
The identification of the unstable daughter particles produced by the cosmic raymuons is through their light emission profiles [105], [107]. They can produce delayed neutrons, which are neutrons associated with the beta decay of the fission
products. Figure 4.6 shows the distribution ofenergies of spallation products.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

Figure 4.6: Distribution of the134

energies of spallation daughters. From [16].

In particular, -ray energy from the decay of nitrogen-17 is in the energy range ofinterest for the pre-Supernova analysis.
These isotopes should be efficiently identified by spallation reduction methods
in Super-Kamiokande [106], which introduces 10% dead-time with 95% reduction
efficiency, making Spallation backgrounds small compared to other backgroundsources.

4.3

Event Selection

Previous studies [16] for the sensitivity of pre-Supernova neutrinos in SuperKamiokande with Gadolinium included the analysis of over 6000 hours of WIT

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

135

data for the intrinsic background estimations, accidental coincidences and fakeneutrons. Also, simulations of TNC on gadolinium were generated for estimationsof the other background sources described in section 4.2 and to calculate expectedsignal of pre-Supernova neutrinos.
As already mentioned in this chapter, changes had to be applied to the previous analysis in order to improve the detection capability and update backgroundmodels. Since [100] was published, new requirements of activity levels of radioisotopes were defined, more currently information regarding reactor activity in Japan
became available, and most importantly, gadolinium sulfate was loaded in SuperKamiokande. The goal of this work is the creation of an online alert system, sothat the event selection had to be built around the best possible system efficiency
and processing speed.
At the time of writing, the software SKDETSIM for SK-Gd was not ready, so thesimulations were handled as described in Section 3.9.4: for TNC on Gadolinium
it is used both the -ray models GLG4SIM from Chooz experiment and the one
created by ANNRI at J-PARC. For coincidence events, the simulated TNC on
Gadolinium events were combined with simulated positrons with SKDETSIM.
These coincidence events were simulated in di↵erent positions in the inner detector,
with positrons with energies between 0.8 MeV and 7 MeV.
The pre-Supernova alert system will work with an online search of coincidenceLow Energy Inverse Beta Decay Detection in Super-Kamiokande

136

events throughout WIT data. Single neutron events take more time for processing,so they will be only used as cross-check for the alert system. For this reason,
improved techniques for event selection are for the DC channel and the updated
results of sensitivity for pre-Supernova neutrino detection in SK-Gd will be given
for only coincidence events.
Multivariate methods are used for selection. In particular Boosted Decision Tree
(BDT) methods are used at two di↵erent levels of the data processing for bothspeed and efficiency reasons.
A pre-selection is applied to candidate events. For single neutron events, besides
being inside the FV, a minimum number of hits is required by applying cuts tothe variable n18 and also volume cut around calibration sources in the detector
is applied. Tests from [16] showed that events with n18 < 26 and ef f wall < 800
were almost never selected, so these cuts are also included in the pre-selection.
The final selection for single neutron events is based on the BDT, as discussed infurther sections.

4.3.1

Selection of Coincidence Events

The alert system will search for coincidence candidate events in WIT data. The
prompt and delayed parts of the event are independently reconstructed.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

137

These events are characterized by two variables, dR and dT, which are thedi↵erence in reconstructed position and time between the prompt and delayedparts of a coincidence event. When comparing accidental coincidences with signal,these variables have very di↵erent distributions, as shown in Figure 4.7.
Candidates are initially chosen using the number of hits n18 and the BONSAI
variables reconstructed position (bx, by and bz) and reconstructed time (bt). Figure 4.8 shows the variables used for the search of pair candidates. These variables
are available online after the events are saved by the WIT system, as describedin Section 3.7.1. Not only they are used to the search for coincidence candidates,
but also for training a BDT, which is called BDTonline , which will be described in
Section 4.4.3.
The search for potential coincidences is performed by looping over all events.
For each prompt event candidate, a delayed candidate event is searched in a time
window from -17 to 290 µs relative to the prompt candidate. If the delayed
candidate has n18 > 13 and the events are inside the FV, the variables dR anddT for the pair candidate is calculated.
The pair candidates need to be inside the ranges 0 < dR < 300 cm and 50 <dT < 300 µs. A cut is applied to the BDTonline score, optimized for the 0.02%
Gd2 (SO4 )3 · 8H2 O loading.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

Figure 4.7: Variables dR and dT for simulated signal events (black) and
accidental background (red). From [16].

138Low Energy Inverse Beta Decay Detection in Super-Kamiokandebx Initial Search (coincidences)139

by Initial Search (coincidences)
Primary fileneutron cut
positron cut

104

Primary fileneutron cut
positron cut

104

10

3

10

3

2

10

2

10

−1500

−1000

−500

0

500

1000

1500

−1500

−1000

bz Initial Search (coincidences)

−500

0

500

1000

1500

bt Initial Search (coincidences)
Primary fileneutron cut
positron cut

104

Primary fileneutron cut
positron cut

4

10

3

10

3

10

2

10

2

10

−1500

−1000

−500

0

500

1000

1500

0

5000

bgoodness Initial Search (coincidences)
5

10

4

15000

20000

n18 Initial Search (coincidences)

Primary fileneutron cut
positron cut

10

10000

Primary fileneutron cut
positron cut

5

10

4

10
3

10

3

10
2

10

2

10
10

1
0

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1

10

15

20

25

30

Figure 4.8: Variables from BONSAI Online reconstruction (bx, by, bz, bt,
bgoodness) and n18. The black solid line shows the variables before any cut,the red dashed line shows the variables for coincidence positron events and blue
for coincidence neutron events after the Initial Search.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

140

Many optimizations were made to the search for pair candidates. The introduction of the BDTonline made the process much faster and increased the expected
sensitivity, as will be shown in the next chapter. Also more strict cuts were applied
for a better choice of candidates.
In the analysis, neutrons are assumed to be captured at the same point of
production. The assumption is reasonable as the distance travelled by the neutron
is much smaller than the position reconstruction resolution [16]. The pre-selection
described for single neutron events is also applied to the neutron part of coincidence
events. Figure 4.9 shows the variables dR and dT before and after the pre-selection
for a random data sample.
The cuts applied during the search for pair candidates and the pre-selection
are not enough to reduce backgrounds. For that reason Multivariate Selection
methods are implemented in the analysis.

4.4

Multivariate Selection methods

The reduction of backgrounds for the pre-Supernova analysis is performed by applying both rectangular cuts and a particular multivariate selection method, theBoosted Decision Tree (BDT). BDTs are machine learning techniques consistingof tree structured classifiers used for discriminating events as signal or background.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

141

dR Initial Search
610dR after initial searchdR after pre-selection

5

10

104
3

10

102
10
1
10−1
0

200

400

600

800

1000

dT Initial Search
510dT after initial searchdT after pre-selection

4

10

3

10

102
10

1
10−1
0

3

20

40

60

80

100

120

140

160

180

×10
200

Figure 4.9: Variables dR and dT calculated after the pre-selection.

BDTs have many benefits when compared to other multivariate selection methods. An artificial neural network is expected to perform better than BDTs, butthey need large CPU times. Also, they require large data samples. BDTs on theother hand are fast, easy to tune, and have very reasonable performances. The
trainings of BDTs are done using the Toolkit for Multivariate Analysis (TMVA)[108] integrated in the ROOT software. Simulated signal was produced using the
GLG4SIM -ray model, already discussed in the previous chapter. For backgroundsamples, data from SK-IV was used.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

142

Rectangular cuts assume an important role during event selection for the initial
searches and the pre-selection applied, as already discussed in Section 4.3. However, even with optimized rectangular cuts, they are not enough the reduce thebackgrounds. A more sophisticated and efficient method was developed in [16]using BDT for further reductions, which was used to estimate the sensitivity of
pre-Supernova neutrinos in SK-Gd.
For the development of the pre-Supernova alert system, another BDT wastrained using only online variables, in order to improve the data processing time.
Discussions and results will be shown in future sections. First a more explained
introduction to BDT is given.

4.4.1

Boosted Decision TreeA decision tree is a pattern recognition technique that classifies events as signal
or background. The concept of a decision tree is to not reject right away events
that fail a specific criterion, searching instead whether other criteria may be more
appropriate to classify these events properly. In this way, a decision tree creates amodel to predict the target value of a variable by learning decision rules from theobservation of this variable. Decision trees are rooted binary trees, with only twoclasses, signal and background.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

143

The classifier begins with an initial note, the root node, which represents thewhole sample to get divided in homogeneous sets, and splits it into a tree structure,
as illustrated in Figure 4.10, in which a decision is made in each node for a di↵erent
discriminating variable, aiming to classify each event as signal or background. Eachnode can be recursively split into two daughters or branches, until some stopping
condition is reached. For each split, the discriminating variable is the one that gives
the best separation between signal and background. At each node, all variables
can be considered, even if they have been used in the previous iteration. Thus,
same variables can be used more than once, while others might not even be used.
The split of nodes are created recursively until it reaches a stop condition,
inserted by the user, declaring such node as terminal leaves. For decision trees,
the CPU consumption time scales as nN log(N ), for n variables and N training
events.
In each node, a cut will be applied to these variables, resulting in the split of
the samples into two leaves. Defining the Gini coefficient G as:

G = p(1

p),

(4.3)

where p is the signal purity p = s/(s + b), namely the probability of an event beingclassified as signal, and (1p) is the background purity. The cut in each node ischosen in a way the maximizes the di↵erence between the Gini coefficient, which

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

144

Figure 4.10: Output from the TMVA software [108] showing a random decision tree that was used for the training for signal and background separationin the pre-Supernova neutrino analysis. Further discussions regarding the discriminating variables used in this scheme will be held in next sections.

gives the probability of misclassification, of the original leaf and the split leaves(weighted by the proportion of events).
In a BDT, the concept of decision tree is extended from one to several trees.
They are derived from the same training ensemble by reweighting events, resulting
in a final classifier that considers the average of each individual decision tree. Thisis very relevant for fluctuations in the training sample, given a better performance
than a single tree [108]. There are di↵erent methods used for boosting, for example
AdaBoost [109], Gradient Boosting [110] and NGBoost [111]. The one chosen forthis study is AdaBoost (adaptative boost), in which, for every new decision tree,
data that were misclassified in the previous one are given an adjusted weight forLow Energy Inverse Beta Decay Detection in Super-Kamiokande

145

the following tree.
The weight of each event in the new decision tree is multiplied by the boost
weight ↵boost , given by equation 4.4.

↵boost =

✓

1

✏
✏

◆ boostwhere ✏ is the misclassification rate of the tree and

,

(4.4)

boost is the boost rate related

to the learning rate of the classifier, inserted by the user.
The event classification yboost (x) of N classifiers combined using AdaBoost is
given by equation 4.5.
Nyboost (x) =1 X
ln(↵i )hi (x),
N i

(4.5)

where ↵i is the weight of the ith ensemble and hi (x) is the classification for the tree
i for the input variable x. Small values of yboost (x) means the event is backgroundlike, while large values means signal-like.
To compare the efficiency between di↵erent BDT trainings, and also other classifiers, a classic method is to get the area under the plot of background rejection
against signal efficiency, known as the Response Operator Characteristic (ROC)
curve. Figure 4.11 shows an example of these curves for di↵erent classifiers. In
the figure, BDT is compared to rectangular cuts and Fisher Linear Discriminant,Low Energy Inverse Beta Decay Detection in Super-Kamiokande

146

Figure 4.11: ROC curves example for di↵erent classifiers. For this example,
in comparison to the Boosted Decision Tree (BDT) ROC curve, are the Fisher
Linear Discriminant (Fisher) and the rectangular cuts (Cuts).

which is a method to perform event selection by distinguishing mean values ofvariable distributions for signal and background [108]. The Fisher Linear Discriminant is an optimal classifier that creates an axis in each input variable hyperspace,pushing background and signal events in this axis as far away from each other as
possible. Figure 4.11 shows that the Fisher Linear Discriminant has a lower efficiency when compared to the BDT for the pre-Supernova analysis.

4.4.2

Previous Estimations

As stated before, in the previous study of pre-Supernova neutrinos [100], a BDT
was used to predict the sensitivity of these neutrinos in Super-Kamiokande. For theLow Energy Inverse Beta Decay Detection in Super-Kamiokande

147

pre-Supernova alert system creation, some optimizations were performed, as wellas the development of a new BDT using only online variables. Before discussing
the currently used BDTs, it will be summarized in this section the main featuresand variables considered for the BDT used in [100].
For the training and testing of the BDT, representative SK-IV real pure water
data were used, extracted as random sub-samples of many di↵erent runs. The
training on broader samples had poor performances.
As for the resources available, the memory was limited to 4096 MB, and because the TMVA software loads entire event tree into memory while running, thenumbers of used variables and events were limited (107 events per 10-20 variables).
Di↵erent values for the parameters to train the BDT were tested to find thebest overall performance, in particular N T ree andboost , which are the number oftrees in the BDT structure and the learning rate of the AdaBoost algorithm [108].
The number of trained events for both signal and background were varied, so as
the variables used for training. Details regarding di↵erent trainings and tests canbe found in [16].
The variables considered, in order of importance to the classifier, for the BDT
used in [16] are:

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

148

fromwall : distance from the reconstructed vertex to the detector wall. Radioactivity
close to the wall generates background events;e↵wall : distance in reconstructed direction from reconstructed vertex to the detector wall. Again background events from radioactivity with direction pointing
towards the nearest PMT;
MSG : Multiple Scattering Goodness (MSG), measure of hit pattern. Events froma single multiple scattered electron/positron have high score, while signal
has low score (multiple gamma rays);cgoodness : ClusFit goodness of fit. Some background events do not fit well due tobeing comprised of a high proportion of dark noise;bz : reconstructed vertex vertical location. Background is more likely to befound in the bottom of the detector due to the pattern of contamination;
pclose : proportion of hit pairs with close angle, backgrounds have hits that areclose in angle to each other;bsdirks : BONSAI reconstructed direction KS test;
Beta[1] : Isotropy parameter. Isotropic distributions of hits have scores closer tozero. The neutron capture on Gadolinium produces more isotropic hit patterns.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

149

mode Ch3hit : binned model opening angle of 3 hit combinations. Background events are
more likely to contain opening angles close to ⇡/2;mean Ch3hit : Mean three hit cone cosine. Events in which the hits were produced by
Cherenkov emisson will have cosine angles close to the Cherenkov angle;
Beta[3] : Isotropy parameter, similar to Beta[1];cldirks : ClusFit reconstructed direction KS. Test of the hit pattern against expected
for single electron in reconstructed direction. Lower score for capture on
gadolinium;bgoodness : BONSAI goodness of fit, similar to cgoodness;
n18 : number of selected online hits.

Regarding the performance of the BDT, it was much better than rectangularcuts and linear discriminant methods, as showed already in Figure 4.11.
The strategy in [16] has been used as a preliminary step and it was followedby many attempts to get a new classifier with improved efficiencies. A BDT witho✏ine variables with improved efficiency was achieved and, among all attempts,
some trainings were performed using only online variables, which are variables
available after the triggering. They are further referenced as BDTonline .

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

4.4.3

150

BDTonline

As it will be shown in Chapter 6, the pre-Supernova alert system is an online
system installed in Super-Kamiokande that needs to process many data sets looking for IBD events candidates and run evaluations to make alarm decisions andsend alerts. For that reason, the system needs to process data as fast as possible,
but without losing detection efficiency. With that in mind, the BDTonline wasdeveloped.
Due to the complexity of the BDT trained with o✏ine variables, it is natural
to expect that it would be more efficient than a BDT trained with only online
variables. Figure (4.12) shows two BDTonline ’s ROC curves, which di↵er fromeach other by the choice of parameters N T rees andboost

and used variables,with the second BDTonline having a better performance. They were trained to get
efficiencies close to the one with o✏ine variables. The performance of the BDTonline
reaches a limit of efficiency at certain point. However, both of the BDTs, online
and o✏ine, can be used during di↵erent stages of the data processing.
The importance that each variable used by the chosen BDTonline has for theclassification is listed in Table (4.3). Also in this case, the boosting method chosen
was AdaBoost, but the choice of parameters was di↵erent to the BDT with o✏ine
variables. In particular, the most efficiency BDT had the parameters: N T ree =1000,

boost = 1.0. To have a less stricted BDT, cuts for training and testing were

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

151

Backgr rejection (1-eff)

ROC curves
1

0.95
0.9

0.85
0.8

Reproduced
BDT with offline variables
BDTonline #1
BDTonline #2

0.75
0.7
0.65
0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95
1
Signal eff

Figure 4.12: ROC curves from reproduced BDT used in [16] (black), the new
BDT with o✏ine variables used for Final Selection (blue) and two di↵erent trials
to get the best performance out of a training using only online variables (green
and yellow).

chosen for a broader event selection than what is done by the BDT with o✏ine
variables. For that, it was extended the range of allowed hits, which characterized
by the variable n18.

Low Energy Inverse Beta Decay Detection in Super-Kamiokande
Online variables BDT
−1

10

−2

10

Signal−3

10Background

−4

10

−5

10

−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8
1
BDT score

Figure 4.13: Background and Signal separation for the BDT with online variables.
Table 4.3: Ranked importance of the used online variables.

Rank

Variable

Importance1

n18

3.949e-01

2bx*bx+by*by

2.768e-01

3bt

1.260-01

4

bgoodness

8.760e-02

5by

7.627e-02

6bx

6.308e-02

7bz

5.055e-02

152

Low Energy Inverse Beta Decay Detection in Super-Kamiokande

153

Figure (4.13) shows the separation between background signal for coincidence
events. For this figure, random WIT data corresponding to approximately 200
hours were used for background sample and for the signal sample simulated coincidence events using the GLG4SIM -ray model.
Initially it was verified that the BDTonline improved the speed of the data processing. A cut was chosen by means of selecting the same number of events
as if the BDT was not applied. Reductions were applied to the same samples
with and without the BDTonline to compare its performance. A random cut on
BDTonline score >0.6 resulted in selecting the same number of events for all

samples that were processed without the BDTonline . However, the process with
the BDTonline was much faster. For di↵erent memory sizes of data being analyzed,
the comparison between the time spent by the system to process data is shownin Table 4.4. Since the data evaluation for the online alert system needs to be
fast, these results are very important. The BDTonline allows the system to achieve
optimal processing speed.
Table 4.4: Approximate time spent to process data for previous approach andoptimized (with BDTonline ).

Number of WIT data subsets

Time spend with previous approach

Optimized

1

3 minutes

40 seconds

10

18 minutes

1.5 minutes

100

3 hours

4 minutes

Low Energy Inverse Beta Decay Detection in Super-Kamiokande
The previous cut on BDTonline score >154

0.6, to evaluate its speed performance,

is not the best cut choice for the signal background separation, which meansthat the BDTonline can also be used to improve the sensitivity of pre-Supernova
neutrinos in Super-Kamiokande. Optimized results, consequent of the applicationof di↵erent cuts on the BDTonline score, will be shown in the next chapter.

Chapter 5

Pre-Supernova Neutrino
Sensitivity at SK-Gd

Previous sensitivity results for detection of pre-Supernova neutrinos at SuperKamiokande with 0.2% Gd2 (SO4 )3 · 8H2 O by mass were given in [100]. However,the first phase of SK-Gd has 0.02% Gd2 (SO4 )3 · 8H2 O by mass and the development of a Supernova alert system based on the detection of pre-Supernova neutrinos required a dedicated response model for the current phase. In this chapter,
sensitivity results to pre-Supernova neutrinos and estimations for the alert systemin the first phase of SK-Gd will be shown. General details about the system itself
will be covered in next chapter.

155

Pre-Supernova Neutrino Sensitivity at SK-Gd

5.1

156

Previous Estimations

In [100], sensitivity results were given for the loading in SK of 0.2% Gd2 (SO4 )3 ·8H2 O by mass, which is the goal for the final concentration of gadolinium inSuper-Kamiokande. Cuts were chosen to give the greatest range of detectionof a 25 M pre-Supernova start for the Odrzywolek model [71], 0.1 hour before
the core-collapse. These choices were based on the facts that real coincidences
would be close together in time and space and that events with higher BDT scoreare more like the model of TNC on gadolinium. The cuts were: BDTscore > 0,
coincidence distance dR < 300cm, and coincidence time dT < 100µs. Figure 5.2
shows the distributions of these variables before and after cuts for signal and main
backgrounds.
As for the single neutrons channel, the final selection was made only with the
BDT, which score had to be greater than 0.56. Figure 5.1 show the distributionof the variable for this channel. Trigger and selection efficiency were found to be
4.3-6.7% for coincidence events and 9.5-10% for single neutron events for the last
12 hours before core-collapse. The uncertainty was given by di↵erent neutrino
spectrum models.
The results were also a combination of the single neutron and DC channels,
as they are highly correlated. The signal window chosen to perform statisticalevaluations was 12 hours. Results are dependent on neutrino mass ordering, ZAMSPre-Supernova Neutrino Sensitivity at SK-Gd

Figure 5.1: Distribution of the variables from the DC channel in the final
selection for [16] results.

Figure 5.2: Distribution of the BDT score for the single channel in the final
selection for [16] results.

157Pre-Supernova Neutrino Sensitivity at SK-Gd

158

mass of the star, distance to the star, and background level, and were given basedon the red supergiant star ↵ Ori (Betelgeuse) as it is currently the best candidatefor detecting pre-Supernova neutrinos due to its mass and distance from the Earth.
Table 5.1: Number of expected signal events from pre-Supernova star at
200 parsecs in the final 12 hours before core-collapse at SK-Gd with 0.2%
Gd2 (SO4 )3 · 8H2 O, from previous estimations [16].

Model

Mass (M )Mass Ordering

Single neutron

DC

Odrzywolek

15

Normal

55-71

33-36

Odrzywolek

15

Inverted

16-20

9-10

Odrzywolek

25

Normal120-160

59-65

Odrzywolek

25

Inverted

34-44

17-18

Patton15

Normal

65-84

45-50

Patton15

Inverted

18-23

13-15

Patton30

Normal

130-170

100-110

Patton30

Inverted

40-52

34-37

Table 5.1 summarizes the expected number of signal events from pre-supernova
stars at 200 parsecs away in the last 12 hours before core-collapse and the estimatedbackground for a 12-hour window in SK-Gd. Tables 5.2 and 5.3 show the most
optimistic range of detection of pre-Supernova stars and the early warning inSuper-Kamiokande, with models following predictions of mass and distance forPre-Supernova Neutrino Sensitivity at SK-Gd

159

Betelgeuse. Scenario uncertainties are used, for di↵erent TNC on gadolinium
models and di↵erent background rate estimations.
Table 5.2: Maximum range of detection of pre-Supernova stars by SK-Gd with
0.2% Gd2 (SO4 )3 · 8H2 O, from previous estimations [16].

Maximum Range for
Model

Mass (M )FPR = 1/year

FPR = 1/cy

(pc)

(pc)

(pc)

Odrzywolek

15

Normal300-400

250-300

Odrzywolek

15

Inverted160-200

130-200

Odrzywolek

25

Normal

330-400

280-400

Odrzywolek

25

Inverted180-200

150-200

Patton15

Normal420-600

360-500

Patton15

Inverted

220-300

190-200

Patton30

Normal480-600

410-500

Patton30

Inverted

270-400

230-300(pc)Pre-Supernova Neutrino Sensitivity at SK-Gd

160

Table 5.3: Expected early warning in SK-Gd with 0.2% Gd2 (SO4 )3 · 8H2 O,from previous estimations [16].

Warning (hours) for
Model

Mass/Distance

Mass Ordering

FPR = 1/year

FPR = 1/cy

Odrzywolek

15M /150pc

Normal5.3-8.4

3.4-6.3

Odrzywolek

15M /150pc

Inverted

0.1-2.0

0.0-0.8

Odrzywolek

25M /250pc

Normal4.7-7.4

3.3-5.7

Odrzywolek

25M /250pc

Inverted

0.0-0.6

0.0-0.0

Patton15M /150pc

Normal7.1-14.1

5.1-9.8

Patton15M /150pc

Inverted

0.3-4.1

0.0-2.2

Patton30M /250pc

Normal

1.0-1.6

0.7-1.1

Patton30M /250pc

Inverted

0.1-0.4

0.0-0.1

The plan for the pre-Supernova alert system is to implement it starting fromthe first phase of loading, which is 0.02% Gd2 (SO4 )3 · 8H2 O by mass. For thatreason all the methods were optimized for the lower loading and will be eventually
improved for next phases of SK-Gd. A few estimations for the first loading werealso given in [16], but for the same background rates as the 0.2% loading. A fullevaluation of the sensitivity for the first phase of SK-Gd is the subject of this work
and it will be shown next.

Pre-Supernova Neutrino Sensitivity at SK-Gd

5.2

161

Results for First Gadolinium Loading

As described in Chapter 3, it was loaded to the water in the Super-Kamiokande
detector 0.02% Gd2 (SO4 )3 · 8H2 O by mass. [100] predicted that, for SK-Gd with aconcentration of 0.2% Gd2 (SO4 )3 ·8H2 O by mass it would be possible the detectionof pre-Supernova neutrinos, even when considering higher background rates fromdi↵erent sources. However, the first phase of SK-Gd has a lower concentration ofgadolinium sulfate, and in order to understand the viability of developing an alert
system for Supernova based on the detection of pre-Supernova neutrinos, their
sensitivity for the first phase of SK-Gd is evaluated in this section.
Response models were created for the 0.02% loading. The TNC on gadolinium
at this concentration has an efficiency of 50%, instead of 90% aimed for the 0.2%
loading [92]. Even though the signal efficiency is lower, background rates due
to radioisotope contamination are also reduced. For instance, one of the main
backgrounds sources of the coincidence channel are pairs of neutrons, which aresignificantly less due to a lower contamination when compared to higher loadingsof gadolinium sulfate. Also, all the backgrounds are reduced after introducing
the BDTonline . The lower concentration of gadolinium sulfate has also a longer
capture time, of about 120 µs, while for the 0.2% loading this time is expected to
be around 30 µs.

Pre-Supernova Neutrino Sensitivity at SK-Gd

162

Only the DC channel was used to estimate the sensitivity. As it was explainedin the last chapter, the alert system will look for coincidence events in the WIT
data, so the sensitivity evaluations need to be consistent to the methods appliedin the system. Single neutron events might still be used for cross-check in case of
potential alerts. Uncertainties using di↵erent models for TNC in gadolinium arecalculated, even though they are not useful for statistical evaluation. Discussions
regarding uncertainties will be held in the next chapter. The final selection used
for the sensitivity studies, and also applied by the alert system, will be discussed
next.

5.2.1

Final Selection

With the implementation of the BDTonline described in Chapter 4, it was possible
to improve the sensitivity estimations. Many cuts were tested to find the optimal
cut to the BDTonline score. The best was BDTonline score >0.2. Anotherimportant feature is that the higher the cut in the BDTonline , the faster the dataprocessing will be, as less events will 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 get the longest early warnings possible for the alarm. For alower gadolinium loading, it is expected a longer coincidence time. Many cuts on

Accidental
Two Neutrons
Signal

7

10

6

10

5

10

Events per 4e-03

Events per 0.1 MeV

Pre-Supernova Neutrino Sensitivity at SK-Gd

163

Accidental
Two Neutrons
Signal

7

10

6

10

5

10

4

10

4

10

3

10

3

10

2

10
2

10

10
10
2

3

4

1
−1

5
6
7
8
9
10
Prompt Reconstructed Total Energy (MeV)

7

10

Accidental
Two Neutrons
Signal

6

10

5

10

4

10

Events per 1.7e+03 ns

Events per 10 cm

1

−0.8

−0.6

−0.4

−0.2

0

0.2
0.4
0.6
0.8
1
BDT Score of Delayed Events

Accidental
Two Neutrons
Signal

6

10

5

10

4

10

3

10

3

2

10

10

2

10

10

3

0

100

200

300

400

500

600 700 800 900 1000
Coincidence Distance (cm)

1
0

20

40

60

80

100

120

×10
140 160 180 200
Coincidence Time (ns)Figure 5.3: Distribution of variables in the coincidence channel. Solid lines
show before the final selection and dashed lines after.

the BDT with o✏ine variables, used for final selection, were tested to get the best
efficiency possible.
The optimized cuts used for final selection are: dR < 300cm, dT < 150µs,
and BDT score > 0.3. All of these cuts, including the one in BDTonline , aredi↵erent for other concentrations of gadolinium. Figure 5.3 shows the distributionof variables before and after applying the final selection.
The remaining backgrounds for the DC events per 7 hour window are shown in
Table 5.4.

Pre-Supernova Neutrino Sensitivity at SK-Gd

164

Table 5.4: Remaining background events per 7 hour window for the DC events
after final selection.

Source5.2.2

Number of Events (/7 hours)

Accidental coincidences

1.44

Pairs of neutrons

1.25

Spontaneous fission coincidences

0.02

Reactor neutrinos and geoneutrinos

0.23

Statistical Parameters

Before moving to the results for the pre-Supernova neutrino sensitivity at thefirst phase of SK-Gd, a few statistical considerations need to be discussed. Some
important statistical parameters need to be taken into account, such as signal andbackground time-windows, hypothesis tests and false positive rates.
The statistical evaluation that will be done in the alarm system is also used toprovide the early warnings and estimate the range of detection of pre-Supernova
stars. The chosen approach is to look at this as a Poisson Counting Experiment[112], calculating the p-value for hypothesis testing. False Positive Rate (FPR) also
needs to be considered in order to take into account the rate in which Supernovae
happen at a detectable distance. Choices of FPR for the alert system will be
discussed in the next chapter.

Pre-Supernova Neutrino Sensitivity at SK-Gd

165

Consider the hypothesis in which only background is present in a sample, callednull hypothesis H0 . A good signal measurement would consist having the sampleinconsistent with this hypothesis. The probability of a test statistic to have avalue consistent with the null hypothesis is called p-value.
The p-value will have very small values in the presence of signal, which leads tobelieve that the sample actually belongs to an alternative hypothesis H1 .
For a counting experiment, the p-value is the probability of counting the numberof events greater or equal to the case where we have no signal, only background.
One way to calculate the p-value of the total number of selected events inside a
pre-defined time window Nevents , for an expected number of background events
NBG is:p = P (Nevents

NBG ) =

1X

P ois(n; NBG )(5.1)

n=Nevents

where P ois(n; NBG ) is the Poisson distribution of the variable n with average NBG .
The evidence of a signal is if the signal has a significance of at least ”3 ” and a
discovery is for ”5 ”. The significance level Z can be evaluated from the p-value
from:
p=

Z 1
Z

1
2
p e x /2 dx = 1
2⇡

(Z)

(5.2)

Pre-Supernova Neutrino Sensitivity at SK-Gd

166

For Poisson counting experiments in which the number of events Nevents has theform:
Nevents = s + b(5.3)

were s is the expected number of signal events and b is the expected number ofbackground events, the significance level can be approximated to:

Z=

s 

2 (s + b)log(1 + s )
b

s

(5.4)

The significances are then evaluated using Equation 5.4. For di↵erent Betelgeuse models, it is found which is the best time window to perform the statisticalevaluations.
Single neutron events can be used as a cross-check in case of positive alerts fromthe alarm system. In this case, the Stou↵er’s method [113] can be used to combine
the significance for both channels.
The results for the updated sensitivity for pre-Supernova stars at 0.02% loading
SK-Gd will be shown next.

Pre-Supernova Neutrino Sensitivity at SK-Gd

5.2.3

167

Results

The results are dependent on the models for pre-Supernova being considered, whichchange in neutrino mass ordering, ZAMS mass of the star, distance to the star,and star evolution model, which was discussed in Chapter 2. The models for TNC
on gadolinium being used are the -ray models GLG4SIM from Chooz experimentand the model from ANNRI at J-PARC.
The background rate used for the evaluation of the sensitivity at the 0.02% loading SK-Gd was predicted using data for the accidental coincidences backgrounds
and simulations for other sources like reactor and geo neutrinos and pairs of neutrons. As already discussed in last section, with the introduction of the BDTonline ,optimizations of other cuts and the updates performed to the response models, theirreducible background rate is 0.306 events/hour. Further discussions regarding
uncertainties and updates will be held in next chapter.
↵-Ori (Betelgeuse) is currently the best candidate for detecting pre-Supernova
neutrinos. In current estimations, its mass and distance from the Earth are correlated, and for that matter, the results are given in combinations of: 150 pc for
15M

and 220 pc for 25M[114; 115]. The signal window chosen to performstatistical evaluations was based on which the best significances for detection of
pre-Supernova neutrinos from Betelgeuse would be achieved, which is 7 hours.

Pre-Supernova Neutrino Sensitivity at SK-Gd

168

Table 5.5 shows the expected number of events of DC events at 200 pc awayin the final 7 hours before the core-collapse. Results are shown for the GLG4SIM
and ANNRI -ray emission models, as discussed in Section 3.9.4. Inverted orderneutrino mass models have very low number of expected events and for that reason, significance levels evaluated for these models are almost always below any
acceptable False Positive Rate, apart from the model Patton with 30M , which isunrealistic for this distance. This was already predicted by [16] for the first phaseof SK-Gd, but it won’t be the case for the next phases.
Table 5.5: Number of expected signal events from pre-Supernova star at
200 parsecs in the final 7 hours before core-collapse at SK-Gd with 0.02%
Gd2 (SO4 )3 · 8H2 O. Results are shown for two -ray emission models.

Model

Mass (M )Mass OrderingDC

Detection Significance ( )Odrzywolek

15

Normal13.83 - 15.42

5.54 - 6.04

Odrzywolek

15

Inverted

3.92 - 4.38

1.95 - 2.14

Odrzywolek

25

Normal24.40 - 27.19

8.55 - 9.27

Odrzywolek

25

Inverted

6.92 - 7.72

3.17 - 3.47

Patton15

Normal19.14 - 21.33

7.12 - 7.73

Patton15

Inverted

5.57 - 6.21

2.64 - 2.89

Patton30

Normal

47.46 - 52.96

13.84 - 14-64

Patton30

Inverted

15.50 - 17.30

6.06 - 6.60

Pre-Supernova Neutrino Sensitivity at SK-Gd

169

Table 5.6: Maximum range of detection of pre-Supernova stars by SK-Gd with
0.02% Gd2 (SO4 )3 · 8H2 O, for normal ordering neutrino mass models. Results
are shown for two -ray emission models evaluated over a 7-hour window.

Maximum Range for
Model

Mass (M )>3

FPR = 3.2/cy

FPR = 1/cy

>5

(pc)

(pc)

(pc)

(pc)

Odrzywolek

15

310-320270-280260-270230-240

Odrzywolek

25

400-420350-370

340-360

300-310

Patton15360-380

320-330300-320270-280

Patton30

560-590490-510470-490410-430

Figure 5.4 shows the evolution in significance level prior to the core-collapse
for normal ordering neutrino mass models. As expected, the event rates increases
significantly closer to the explosion, leading to greater significances right before
the Supernova.
The maximum range of detection of pre-Supernova stars are summarized inTable 5.6. Expected early warnings for the pre-Supernova alarm are shown in
Figures 5.5 and 5.6 and 5.7 shows the early warning for Betelgeuse-like models.
Di↵erent false positive rates are being considered in these results, as they will be
important for further discussions about alarm decision.

Pre-Supernova Neutrino Sensitivity at SK-Gd

170

Figure 5.4: Evolution of significance level for the normal ordering neutrinomass models for stars at 200 pc in the last hours before the core-collapse at
SK-Gd with 0.02% Gd2 (SO4 )3 · 8H2 O.

From these results we see that, for neutrino normal mass ordering, significantly high significance levels are expected in the first phase of SK-Gd with 0.02%
Gd2 (SO4 )3 · 8H2 O by mass, allowing to claim discoveries and it is also an evidence
for the neutrino mass ordering. Also, the results in Table 5.5 and Figures 5.5 and5.6 prove the viability of developing an alert system for Supernova based on thedetection of pre-Supernova neutrinos, with potential early warning of five hours.

Pre-Supernova Neutrino Sensitivity at SK-Gd

171

Figure 5.5: Expected early warning above 3 against the distance for 15 M
and 25 M stars for the considered pre-Supernova models, evaluated in steps of
0.01 kparsec. Dashed line shows predictions using the GLG4SIM -ray modeland solid line for the ANNRI -ray model evaluated over a 7-hour window.
Table 5.7: Expected early warning in SK-Gd with 0.02% Gd2 (SO4 )3 · 8H2 O,
for Betelgeuse-like models with normal ordering neutrino mass models. Results
are shown for two -ray emission models evaluated over a 7-hour window.

Warning (hours) for
Model

Mass/Distance>3

FPR = 3.2/cy

FPR = 1/cy

Odrzywolek

15M /150pc

3.1-3.2

2.3-2.4

2.2-2.3

Odrzywolek

25M /220pc

3.4-4.1

3.2-3.3

3.1-3.3

Patton15M /150pc

5.0-5.2

4.1-4.2

3.8-4.1

Pre-Supernova Neutrino Sensitivity at SK-Gd

172

Figure 5.6: Expected early warning considering a rate of 3.2 Supernovae per
century against the distance for 15 M and 25 M stars for the considered
pre-Supernova models, evaluated in steps of 0.01 kparsec. Dashed line shows
predictions using the GLG4SIM -ray model and solid line for the ANNRI -raymodel evaluated over a 7-hour window.

Discussions about the development of the pre-Supernova alarm will be held in
Chapter 7. Next phases of SK-Gd will have greater concentrations of gadolinium
sulfate and a predictions for their pre-Supernova sensitivity will be shown next.

5.3

Future Gadolinium Loading Phases

As already shown in Chapter 4, SK-Gd will have a few phases corresponding
to the concentration of gadolinium sulfate being loaded to the detector. The

Pre-Supernova Neutrino Sensitivity at SK-Gd

173

first phase, with 0.02% Gd2 (SO4 )3 · 8H2 O initiated in 2020 and its sensitivity for
pre-Supernova neutrinos was shown in this chapter. The next proposed phase
is for a concentration of 0.06% Gd2 (SO4 )3 · 8H2 O and the final phase will have
0.2% Gd2 (SO4 )3 · 8H2 O. The EGADS detector is already loaded with 0.06%
Gd2 (SO4 )3 · 8H2 O for simulating the future loading in Super-Kamiokande.
It is expected that for higher concentrations of gadolinium sulfate in SuperKamiokande, the TNC will be more efficient, which will improve the capabilities
for pre-Supernova neutrino detection. However, the higher concentrations carryalso more backgrounds due to radioisotope activities. The change in rates due tothis contamination is shown in Table 5.8.
Table 5.8: Expected rates for the Spontaneous Fission and Pairs of Neutrons
backgrounds for the three phases of SK-Gd.

Rate (/hour ⇥ FV)
Gd2 (SO4 )3 · 8H2 O concentration by mass

Spontaneous Fission

Pairs of Neutrons

0.02%

0.06

0.23

0.06%

0.38

1.37

0.20%

0.62

2.28

These next phases of SK-Gd will benefit of many studies using data from thecurrent stage. For example, models for background evolution in time can bePre-Supernova Neutrino Sensitivity at SK-Gd

174

created and multivariate methods will be trained using gadolinium data, giving
more reliable a selection.
For now, keeping the same cuts and BDT used for the 0.02% Gd2 (SO4 )3 · 8H2 O
loading, as described in Section 5.2.1, a few predictions are made for the next twophases of SK-Gd. Tables 5.9 and 5.10 show the expected range of detection andtables 5.11 and 5.12 show the early warnings for Betelgeuse-like models.
Table 5.9: Maximum range of detection of pre-Supernova stars by SK-Gd with
0.06% Gd2 (SO4 )3 · 8H2 O, for normal ordering neutrino mass models. Results
are shown for two -ray emission models evaluated over a 7-hour window.

Maximum Range for
Model

Mass (M )>3

FPR = 3.2/cy

FPR = 1/cy

>5

(pc)

(pc)

(pc)

(pc)

Odrzywolek

15340-360

300-320290-300

250-270

Odrzywolek

25450-470

400-420380-400

330-350

Patton15

400-420350-370

330-350

300-310

Patton30

610-640

540-560

510-540

450-480

Pre-Supernova Neutrino Sensitivity at SK-Gd

175

Table 5.10: Maximum range of detection of pre-Supernova stars by SK-Gd
with 0.2% Gd2 (SO4 )3 ·8H2 O, for normal ordering neutrino mass models. Results
are shown for two -ray emission models evaluated over a 7-hour window.

Maximum Range for
Model

Mass (M )>3

FPR = 3.2/cy

FPR = 1/cy

>5

(pc)

(pc)

(pc)

(pc)

Odrzywolek

15350-370

310-330300-310260-280

Odrzywolek

25

470-490410-430

400-420350-370

Patton15

410-430360-380

350-370

310-320

Patton30

630-670

560-590530-560

470-500

Table 5.11: Expected early warning in SK-Gd with 0.06% Gd2 (SO4 )3 · 8H2 O,
for Betelgeuse-like models with normal ordering neutrino mass models. Results
are shown for two -ray emission models evaluated over a 7-hour window.

Warning (hours) for
Model

Mass/Distance>3

FPR = 3.2/cy

FPR = 1/cy

Odrzywolek

15M /150pc

4.1-4.2

3.1-3.2

2.4-3.1

Odrzywolek

25M /220pc

4.2-4.3

3.6-4.1

3.3-3.6

Patton15M /150pc

5.2-6.0

4.3-5.1

4.2-5.0

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176

Table 5.12: Expected early warning in SK-Gd with 0.2% Gd2 (SO4 )3 · 8H2 O,
for Betelgeuse-like models with normal ordering neutrino mass models. Results
are shown for two -ray emission models evaluated over a 7-hour window.

Warning (hours) for
Model

Mass/Distance>3

FPR = 3.2/cy

FPR = 1/cy

Odrzywolek

15M /150pc

4.2-5.1

3.2-4.0

3.1-3.2

Odrzywolek

25M /220pc

4.3-5.1

4.0-4.1

3.3-4.0

Patton15M /150pc

6.0-6.1

5.0-5.2

4.3-5.1

In figure 3.16 of Section 3.9, it was shown the improvement of neutron capture efficiency with the increasement of the concentration of gadolinium sulfate inSuper-Kamiokande. Next phase with 0.06% concentration by mass is expected for2022. Many studies are still to be done to improve the sensitivities for these two
next phases.

Chapter 6

Pre-Supernova Alarm

Having determined the expected early warnings, expected detection range, andstatistical tools, the creation of an alert system based on the detection of preSupernova neutrinos was possible. The goal is to have an alarm in Super-Kamiokandethat will send alerts to the community in case of a potential core-collapse Supernova, which is called pre-Supernova alarm.
It was shown in the last chapter the viability to design the system for the firstphase of SK-Gd with 0.02% Gd2 (SO4 )3 · 8H2 O. Details about data acquisition,
event selection, alarm decision, and the design of the system will be shown in thischapter.

177

Pre-Supernova Alarm

6.1

178

Preliminary Considerations

The first version of the pre-Supernova alarm is a quasi-online system. The o✏ine
reconstruction is still being applied to improve the selection efficiency, as the someof the background reductions in Super-Kamiokande are applied o✏ine. Discussions
regarding future plans for online background reductions will be held in Section 6.4.
Because of the impossibility of creating a complete online system for the preSupernova alarm currently, optimizations were performed in order to reach a fastero✏ine reduction for every new data being analyzed by the system. The creationand implementation of the BDTonline was the main accomplishment, followed by
optimization in cuts of other variables and studies of best statistical parameters
for the alarm decisions. A smart and efficient system had also to be developed forstoring only necessary information and using less memory as possible.
Many tests were performed for di↵erent SK data periods and stages of dataacquisition in the WIT trigger. Figure 6.1 shows the data flow for the WIT
system. The alarm is currently installed in the Organizer PC, processing data
right after their acquisition and before being sorted by the Organizer processes,
which assemble all individual data blocks collected into sequential sub-runs (87.381
seconds of data). The organizer takes a significantly time for its processes and forthis reason, the alarm system was best placed before it, having access to new data
instantly.

Pre-Supernova Alarm

179

Figure 6.1: Edited from [83]. Illustration of the acquisition system in SuperKamiokande and where the pre-Supernova alarm is placed.

The alarm system receives constantly a sub-set of events, which then goes
through the reduction and the number of candidate events selected from the subset is saved. All the selected events inside a specific time-window is accounted andthe system performs a statistical evaluation to give an alarm decision.
False alerts are also taken into consideration. For calibration sources inside thedetector it is applied a volume cut around them. Also, when a calibration work
is being performed in Super-Kamiokande, the system will ignore data from that
period. False alerts from a statistical point of view will be discussed in the next
section.

Pre-Supernova Alarm

6.2

180

Alarm Decision

The alarm decision is whether the software should send an alert or not. Many
considerations have to be taken into account:• Background levels;• False Positive Rates;• Alarm efficiency;• Current detector status;• Concentration of loading;• Expected early warning;• Expected range of detection.
After processing new data, the number of selected events needs to go through astatistical evaluation in order to understand what is the significance of the detection. To do it so, we look at it as a Poissonian Counting Experiment, as explainedin Chapter 5.
It is important to notice that the background window used is of 7 hours. The
number of events used for the statistical evaluation also have to account for thePre-Supernova Alarm

181

same time window. The system accounts the number of events in the last 7 hours
to perform all the evaluations and make the appropriate alarm decision.
So, using equations (5.1) and (5.4), the system calculates the significance every
time it receives a new sub-set of data, always being consistent with the chosen
time window. The significance level found is then compared to a False Positve
Rate (FPR), and in case it is above its value, an alert is sent.

6.2.1

False Positive Rates

When testing a hypothesis, one of the outcome may be given by a false positive,
which is the probability of wrongly reject one correct hypothesis. For a preSupernova neutrino detection, this is related to the rate in which a Supernova
may occur within the Milky Way.
To introduce this factor in the statistical evaluation, a cut-o↵ in the p-value isintroduced. For example, if it is expected one Supernova event per year, the false
positive rate (FPR) would be given by:

FPR =

1
= 2.74 ⇥ 10 3
365

(6.1)

This would lead to a minimum significance level of 2.78 when applied to equation
5.2.

Pre-Supernova Alarm

182

However, the literature usually considers this rate if Supernova in our galaxy to
be one per century, which gives:

1
FPR =
100

✓

1
365

◆

= 2.74 ⇥ 10 5 ,(6.2)

which leads to a minimum significance level of 4.03.
The choice of the false positive rate a very important for the alarm efficiency andalso guarantees a high confidence level. Based on [116], the rate of core-collapse
Supernova given is 3.2 per century, in order to account all the Supernovae events
observed over the last millennium, which is a very reasonable rate to be used toapply the FPR rate in the alarm system. The FPR in this case is:

3.2
FPR =
100

✓

1
365

◆

= 8.78 ⇥ 10 5 ,(6.3)

which leads to a minimum significance level of 3.75.

6.3

System design

When initiated, the system gives the options to change the values of backgroundrate (default: 0.306 event/hour) and time window (default: 7 hours) for the statistical evaluation. Figure 6.2 shows the options given to the user after the system

Pre-Supernova Alarm

183

Figure 6.2: Screenshot of the main menu of the alarm system after initialization.

Figure 6.3: Screenshot of the alarm view when data is being processed. Information regarding last evaluations are also showed in the screen.

initialization.
If the user’s choice is to run the system, it will first create a list of all the available
data. A “to do” queue is created for the data that have not been processed yet,which includes new files in the data directory and also files that were modified.
Files in the queue will go through the data processing.
Figure 6.3 shows a screenshot of when the system is processing data. The

Pre-Supernova Alarm

184

events passing the selection will have their information saved: absolute time inwhich these events happened, and information regarding di↵erent variables, suchas the coincidence time and distance. When the queue is empty, the system will
look again for new data. If they are not available yet, it keeps checking for new oraltered sub-sets.
Every time a new sub-set of data is processed, the system calls a function to
perform the statistical evaluation, which accounts the number of events that wereselected in the last 7 hours. If the significance level is above the False Positive
Rate, it sends an email alerting the possibility of a Supernova.
The estimated early warning lead time, which is the interval between the time
when an event really occurs and it is triggered by the WIT system and the time thealert system makes an alarm decision, is summarized in Table 6.1. At the time ofwriting, the WIT system, where the alarm is installed, is currently being upgradedwith new machines, which will reduce the lead time in about ⇠ 10 minutes.

Pre-Supernova Alarm

Figure 6.4: Main web page of the alarm system used to verify latest evaluations and the alarm status.

185Pre-Supernova Alarm

186

Table 6.1: Estimated early warning lead time of the pre-Supernova alert system.

Process

Estimated Time

Data Acquirement (WIT system)

15 minutes

Data Reconstruction

50 seconds

Data Processing

40 seconds

Statistical Evaluation/Alarm Decision

10 seconds

Total⇠ 16 minutes

The results of the evaluation and other information of the alert system is printed
to a web page. Figure 6.4 shows the web page available for the user to follow thealarm status. The system also monitors the rates of IBD pairs and the evolutionof the significance levels found in the last hours. They can be checked at the main
web page and also in more details in a secondary page. Figures 6.5 and 6.6 show
the plots for random values used for testing. They can be used for monitoring
random fluctuations, increments in IBD pairs and significances, which can become
potential alerts.

Pre-Supernova Alarm

Figure 6.5: Screenshot of the secondary web page showing plots for the evolution of evaluated significance levels and selected IBD pairs for di↵erent timeintervals.

187

Pre-Supernova Alarm

188

Figure 6.6: Screenshot of the secondary web page showing plots of di↵erent
variable distributions over the last 12 hours for the events selected by the system.

6.4

Prospects

By the time of writing, the gadolinium loading in Super-Kamiokande is still in
the commissioning phase, which prevents the update of backgrounds estimations
using gadolinium data. After a large sample of SK-Gd data is collected, models

Pre-Supernova Alarm189

of background varying in time will have to be developed. Background rates from
Spallation and Reactor neutrinos ideally need to be tracked and reduced online.
Simulations also need to be updated when the software SKDETSIM for SK-Gd
becomes available, which takes into account current dark rates in the detector andoptimized models for TNC in gadolinium for Super-Kamiokande.
Another main update to the currently used models is to account the systematic
uncertainties. The data files come from the WIT trigger, which has no information
yet regarding the uncertainties related to event reconstruction. Also, some of the
background sources, like Reactor neutrinos fluxes, don’t account uncertainties fortheir fluxes calculations.
With all of these improvements in the future, the alert system can be moved
from quasi-online to completely online.

Chapter 7

Prospects for Low Energy
detection in Hyper-Kamiokande
experimentHyper-Kamiokande (Hyper-K) is the next generation Water-Cherenkov detector
with multi-purpose scientific goals, as the investigation on CP-violation in leptonic sector, determination of the neutrino mass ordering, observation of Cosmic
Neutrinos and Proton Decay. For its high statistics data within a huge fiducial
volume and longevity, Hyper-K has the potential to many discoveries in Neutrino
Astronomy.

190

Prospects for Low Energy detection in Hyper-Kamiokande experiment

191

Figure 7.1: Illustrated scheme of the Hyper-K Detector.

The detector will be a cylindrical tank with 71 meter height and 68 meters
diameter. The total and fiducial masses will be respectively 260 ktons and 187
ktons, which are about ten times larger than Super-Kamiokande. Hyper-K iscurrently under construction and the experiment will be ready for data taking in
2027. There are proposals for the construction of a second tank in Korea.
Accelerator neutrinos measurement from the complex JPARC will be also improved with a higher intensity beam. A new water Cherenkov detector of 1 kton atabout 1 km from the source, the Intermediate Water Cherenkov Detector (IWCD),
will provide flux predictions in Hyper-K and reduce systematic errors related toneutrino cross section uncertainty.
Hyper-K will also have enhanced PMTs with improved performances as newProspects for Low Energy detection in Hyper-Kamiokande experiment

192

geometry, better timing resolution and detector efficiency [117; 118]. The baseline
design foresees 40,000 20-inch photomultiplier tubes (PMTs) observing the Inner
Detector, resulting in a photo-cathode coverage of 40%, and 10,000 3-inch PMTs
in the Outer Detector, which acts as veto for the entering particles. Following therequirements for the Hyper-K detector, alternative designs have been studied inorder to improve the experiment physics capabilities. One of them, the multi-PMT(mPMT) concept, is presented next.

7.1

The mPMT concept

The mPMT module is an array of small PMTs and their electronics arranged insidea pressure resistant vessel. It was first implemented in the KM3NeT experiment[119], which showed advantages like:• Improved segmentation: Single-photon hits can be distinguished from multiphoton hits analysing the signal in neighboring PMTs.
• Superior photon counting: Total number of deposited photoelectrons can bedirectly obtained from hits on PMTs.
• Local coincidences: When considering coincidences between individual PMT
inside the modules, suppression of uncorrelated single-hit noise can be improved.

Prospects for Low Energy detection in Hyper-Kamiokande experiment193• Directional sensitivity: With the individual direction of each single PMT,
the mPMT gives information on the direction of detected photons, improving
signal-to-background separation.
• Extended dynamic range: When multiple photons arrive at the same time,
it will be more likely to hit di↵erent PMTs.

The alternative option for photodetection studied for Hyper-K is the combination of the 20-inch PMTs and the mPMTs modules in the far detector, facing theInner Detector. For the IWCD it is already decided that the detector will use themPMT photodetection system.

7.1.1

mPMT Geometry for Hyper-K

As illustrated in Figure 7.2, the basic geometry of the mPMT module for Hyper-K
is a cylindrical vessel, housing 3-inch PMTs. The PMTs are supported by a 3D
printed structure and optically coupled by Silicon Gel. The vessel has an acrylic
cover and the cylinder is in high density polyethilen and the base is in stainlesssteel. The 3-inch PMT used in the modules will be the Hamamatsu R14374.
The module has a front-end board integrated inside the vessel, as well as HV
suppliers for the PMTs. To each small photodetector is added a reflector cone, inorder to increase the e↵ectiveness of the photocathode area.

Prospects for Low Energy detection in Hyper-Kamiokande experiment

194

Figure 7.2: Illustration of the mPMT module.

Figure 7.3: The two initial prototypes for Hyper-K from INFN (left) andTRIUMF (right).

Designs of mPMT modules for the Hyper-K experiment have been in development, taking into account di↵erent configurations for the PMTs, vessel materials,
PMT read-outs, assembling processes, etc.
The initial prototypes, which are under test, are:

• INFN Prototype: based on KM3NeT experiment, a mPMT module with a
17-inch vessel and 3-inch PMTs inside to test the acrylic vessel and electronics. Figure 7.4 shows the assembling of a prototype in INFN Naples.
• TRIUMF Prototype: optimized design to test of new mechanics and assembling procedure.

195

Figure 7.4: Assembling of the mPMT prototype in INFN Naples.

Prospects for Low Energy detection in Hyper-Kamiokande experiment

Prospects for Low Energy detection in Hyper-Kamiokande experiment

196

Preliminary simulation studies have already showed improved vertex resolution
and increased PID separation compared to 20-inch PMTs [120], which will beshown in Section 7.3.

7.1.2

Acrylic Vessel

Km3NeT experience demonstrated that glass vessels are characterized by high40

K and other radioactive contamination. Tests on acrylic samples from several

companies have been performed to identify the best solution for Hyper-K.
Tests of optical and mechanical performances, absorption, compatibility with
optical gel, radioactive contamination, and pressure resistance, showed that the’PLEXIGLAS GS UV transmitting’ by Evonik is the best solution for the mPMT
module. Results of optical transmittance test in di↵erent acrylic materials are in
Figure 7.5. A hydro-static pressure test has been realized for 15mm and 20mmthick spherical vessels to verify the safe constraint to resist to 1.26 MPa. No
damage has been registered up to 1.8 MPa for both the vessels, much higher thanthe Hyper-K constraints.
Nuclear tests at the National Gran Sasso Laboratories (LNGS) have been performed to investigate radioactive contaminations by measuring gamma emissions,
showing radioactivity levels comparable to the ’KURARAY PARAGLAS-UV00’
acrylic currently used in Super-Kamiokande [121].

Prospects for Low Energy detection in Hyper-Kamiokande experiment

197

Figure 7.5: Acrylic samples transmittance

7.2

mPMT Tests at Memphyno

At the time of writing, the initial mPMT prototype developed in INFN Naples
is under test at the MEMPHYno test bench at the AstroParticule et Cosmologie(APC) in Paris. It consists of a 8m3 filtered water tank with four identical planesof scintillators, two at the top and two at the bottom, to allow the reconstructionof crossing muons. Figure 7.6 shows a picture of the tank. The prototype is being
tested in water since October 18th, 2019.
The 17-inch mPMT module prototype contains old models of 3-inch PMTs,
Hamamatsu R12199, in two di↵erent hemispheres: the ID with 13 PMTs and the
OD with 4 PMTs.

Prospects for Low Energy detection in Hyper-Kamiokande experiment

198

Figure 7.6: Picture of the MEMPHYno test bench at APC in Paris, where
the mPMT prototype developed by INFN Naples is being tested.

Following the requirements for Hyper-K, the the electronics for the mPMT modules needs to take into account low power consumption and good charge and time
resolution. The prototype has as HV supplier a basic Cockcroft-Walton voltage
multiplier circuit, used to generate multiple voltages for the PMT. It raws lessthan 1.5 mA of supply current at 3.3 V and can provide up to -1500 V cathodevoltage.

Prospects for Low Energy detection in Hyper-Kamiokande experiment199

Two di↵erent designs for the mPMT digitization are in development: a Q/T
digitization based on discrete components and one based on Flash Analog to Digital Converters (FADC) digitization, with on-board signal processing. The INFN
Naples prototype current has the digitizer unit based on Q/T digitization, with atime-to-digital converter (TDC) and a 2-step integrator coupled to a sample-andhold analog-to-digital converter (ADC), which showed good performance. The
achieved timing resolution is ⇠100 ps, the charge resolution is 0.1% and good linearity of the circuit response to charge was observed. The Q/T digitization basedon discrete components will be used for the Hyper-K far detector.
The system has very low power consumption, reaching 12.5 mW for the HV
circuit and 40.5 mW for the digitizer per-channel, with a total power consumptionof ⇠ 4W per mPMT module [121].
Recent updates to the main board firmware have been performed in order toimprove measurements and monitoring of the mPMT module.
Tests of the mPMT prototype include: directionality, time response, charge
resolution, environmental measurements, etc. Dark rates are being carefully evaluated, as it a↵ects directly the mPMT reconstruction capability, especially at low
energies. Figure 7.7 shows results from dark rates of di↵erent PMTs inside themodule in respect to threshold.

Prospects for Low Energy detection in Hyper-Kamiokande experiment200

Figure 7.7: Results of dark rate evaluation of di↵erent PMTs in the mPMT
module prototype tests at MEMPHYno.

Strategies are being studied to improve dark counts such as adding coating
material to insulate the PMT tube, reduction of radioactive materials in the PMT
glass, and perform software reductions to dark rates, which will be showed next.

Prospects for Low Energy detection in Hyper-Kamiokande experiment

7.3

201

Reduction of mPMT dark rates with BDT

As showed in section 7.1, the implementation of mPMTs can bring many advantages for neutrino detection.1 Improved vertex resolution will give a higher fiducial
volume, improved energy resolution, etc., which will make the low energy neutrino
detection more efficient. Preliminary results on vertex resolution already showed
improvements with the use of mPMTs. Figure 7.8 shows the improved vertex
resolution and the particle identification for the use of 3-inch PMTs compared to8-inch or 20-inch PMTs.
Simulations studies are currently being implemented to evaluate the detector
performance. The software system for Hyper-K is being developed to be a reliable
and adaptive tool.
The simulation flow consists in generate event topologies by Neutrino interaction
packages (GENIE [123] and NEUT [124]) and modeling them with a Monte Carlo
response code called WCSim. To reconstruct low energy events it is used BONSAI,
same as explained in section 3.7.1, or LEAF. For high energy events, it is usedfiTQun [125].
The Water Cherenkov Simulator, WCSim, is a GEANT4 [97] based code used tosimulate the geometry and physics response of a water Cherenkov detector [126].
When simulating the Hyper-K detector, WCSim reads the type and number ofProspects for Low Energy detection in Hyper-Kamiokande experiment

Figure 7.8: Vertex resolution (top) and electron/muon separation (bottom)
improved by the use of 3-inch PMTs (red) in comparison with 20-inch (blue)and 8-inch (black). From [122].

202

Prospects for Low Energy detection in Hyper-Kamiokande experiment203

photodetectors being used (20-inch PMTs, mPMTs or both), the detector diameter and radius and dark rate information, among other configurations. WCSim
outputs both raw hits (which and how many times the PMTs were hit) and digitized information (number of hits in a trigger window, time and charge). The
events generated by WCSim are then reconstructed. For low energy events it isused BONSAI and, as an alternative option, the Low Energy Algorithm Framework (LEAF), which is is a simple low energy fitter [127].
Multivariate selection techniques such as Boosted Decision Tree (BDT) are currently being applied to simulated events for a detector configuration with mPMTs,
in order to achieve a software reduction of dark counts. Following the description
introduced in Section 4.4, di↵erent samples of background and signal are used inorder to create a BDT classificator.
Figure 7.9 shows the ROC curves of preliminary trials of BDT trainings. For signal events it was used 1 MeV electrons generated with WCSim and reconstructedwith LEAF, while for background events it was used 500 keV electrons, which aremuch below the expected energy threshold in Hyper-Kamiokande, so they can beapproximated as Dark Rate events. This approximation was done due to software
limitations and further implications will be studied. Figure 7.10 shows the signal and background separation to the most efficient one, which had as choice ofProspects for Low Energy detection in Hyper-Kamiokande experiment

204

Figure 7.9: ROC curves for di↵erent trials of BDT trainings to be used forthe software dark rate reduction of mPMTs.

parameters

boost = 0.8 and N T rees = 1000.

Table 7.1 shows the ranked impor-tance of each variable outputted from LEAF for the BDT classification and a brief
description of them.
Variable

ImportanceDefinition

mPMT hits

7.76E-02

Number of hits in the mPMTs.

ID hits 400

7.22E-02

Number of hits in the ID within 400 ns window.

mPMT hits 400

6.99E-02

Number of hits in the mPMTs within 400 ns window.

mPMT hits 200

6.71E-02

Number of hits in the mPMTs within 200 ns window.

ID hits

6.64E-02

Number of hits in the ID.

lf vertex[1]6.35E-02

Reconstructed vertex, Y.

Prospects for Low Energy detection in Hyper-Kamiokande experiment205

lf vertex[0]6.30E-02

Reconstructed vertex, X.

lf vertex[2]6.22E-02

Reconstructed vertex, Z.

ID hits 200

6.22E-02

Number of hits in the ID within 200 ns window.

lf NLL

6.13E-02

Negative Log Likelihood for best vertex candidate.

rawhit num

6.09E-02

Collection of hits, including dark noise.

lf vertex[3]5.69E-02

Reconstructed vertex, T.

mPMT hits 50

5.39E-02

Number of hits in th emPMTs within 50 ns window.

ID hits 50

5.04E-02

Number of hits in the ID within 50 ns window.

lf intime

4.66E-02

Number of hits within a time window used by LEAF.

digithit num

4.24E-02

Number of digitized hits.

lf ctime

2.36E-02

CPU time for event reconstruction.

Table 7.1: Variables in order of importance of the BDT for the software Dark
Rate reduction in Hyper-Kamiokande.

The results from figures 7.9 and 7.10 demonstrate the possibility of reducing
the dark rate of mPMTs even further. At the time of writing, studies are still
being performed to get the most efficient BDT and cut on the BDT score for thesoftware reduction. Other multivariate methods will also be tested.

Prospects for Low Energy detection in Hyper-Kamiokande experiment

206

Figure 7.10: Signal and background separation for the most efficient BDT of
Figure 7.9.

7.4

Detection of Supernova and Pre-Supernova
Neutrinos in Hyper-K

Hyper-K has several advantages compared to the current or planned experiments,
starting from its large volume, which provides high statistics for neutrino events.
Enhanced energy and direction sensitivities, as well as low detection thresholdenergy and longevity will allow a comprehensive study of Supernova neutrinos.
The design of the DAQ and bu↵ering system are being developed to accept abroad range of rates for galactic Supernovae within 10 kpc. Hyper-K can observe

Prospects for Low Energy detection in Hyper-Kamiokande experiment

207

Figure 7.11: Predicted Inverse Beta Decay reactions due to Supernova Relic
Neutrinos in function of years for current and planned experiments, from [128].

50,000 to 75,000 inverse beta decay events, 3,400 to 3,600 ⌫e -scattering events, 80
to 7,900 ⌫e + 16 O CC events, and 660 to 5,900 ⌫¯e + 16 O CC events for a 10 kpc
Supernova, a total of 54,000 to 90,000 events.
The study of Supernova Relic Neutrinos could also be improved with Hyper-K.
The first observation of SRN is expected in SK-Gd, in an energy range of 10-20
MeV. In [128], a few estimations for SRN detection in Hyper-K was performed, inwhich the experiment could measure these neutrinos with energies of 16-30 MeV.
It is expected the selecting of 70 SRN events after 10 years of observations inHyper-K. The expected number of IBD reactions due to SRN are shown in Figure
7.11 in several experiments along the years. These are very preliminary results

Prospects for Low Energy detection in Hyper-Kamiokande experiment208

and a full Monte Carlo analysis for SRN predictions in Hyper-K is still to be
performed.
If Hyper-K will be gadolinium-doped, the lower energy end for the detection of
SRN could be lower as 10 MeV, which is very important to explore the historyof Supernova bursts back to the epoch of red shift (z) ⇠ 1. In addition, a Gddoped Hyper-K will also be able to detect pre-Supernova neutrinos, with great
sensitivity due to a much bigger FV. Also a powerful pre-Supernova alarm could
be developed, as many more events are expected, increasing early warnings andranges of detection compared to SK-Gd. Assuming the same backgrounds for
SK-Gd, simple estimations of a pre-Supernova alarm for Hyper-K were evaluated,adjusting the target mass. Results for early warning and range of detection for a
0.02% Gd2 (SO4 )3 · 8H2 O loading in Hyper-K are shown in Tables 7.2 and 7.3.

Prospects for Low Energy detection in Hyper-Kamiokande experiment

209

Table 7.2: Expected maximum range of detection of pre-Supernova stars inHyper-K loaded with 0.02% Gd2 (SO4 )3 · 8H2 O, for normal ordering neutrinomass models.

Maximum Range for
Model

Mass (M )>3

FPR = 3.2/cy

FPR = 1/cy

>5

(pc)

(pc)

(pc)

(pc)

Odrzywolek

15

850

750

710

630

Odrzywolek

25

1120

980

940

830

Patton

15

990

870

840

740

Patton

30

1560

1370

1310

1150

Table 7.3: Expected early warning in Hyper-K loaded with 0.02% Gd2 (SO4 )3 ·8H2 O, for Betelgeuse-like models with normal ordering neutrino mass models.

Warning (hours) for
Model

Mass/Distance > 3

FPR = 3.2/cy

FPR = 1/cy

Odrzywolek

15M /150pc

11.0

10.0

9.2

Odrzywolek

25M /220pc

10.0

9.3

8.2

Patton15M /150pc

13.0

12.1

12.0

By the time of writing, it is still unclear if Hyper-K will be gadolinium doped
someday. Although many studies are yet to be performed for its pre-Supernova
neutrino sensitivity, these preliminary results shows a very great potential for a
pre-Supernova alarm in Hyper-K.

Chapter 8

Conclusion (English)

The new phase of Super-Kamiokande detector, SK-Gd, is improving the capabilities of the experiment to identify neutrons, which enable the reduction of theenergy threshold for the detection of neutrinos. In the context of this work, thenever-before-detected ⌫¯e from pre-Supernova stars is now possible to be observed
and its detection can provide an early warning for potential core collapse Supernovae.
Previous work [16] evaluated the sensitivity for the detection of pre-Supernova
neutrinos in Super-Kamiokande with 0.2% Gd2 (SO4 )3 ·8H2 O by mass, prior to theloading of a lower concentration the compound in the detector in 2020. Since then,
new requirements for radioisotope contamination of gadolinium sulfate were established, as well as updated information regarding reactor fluxes, which are two of210

Conclusion (English)

211

the main backgrounds for the pre-Supernova neutrino detection. All backgrounds
sources need to be carefully evaluated as the detection of pre-Supernova neutrinos
is below usual energy thresholds of Super-Kamiokande. At the time of writing,the first stage of SK-Gd is still in the commissioning phase and after acquiring agood amount of data, backgrounds will be better determined and evaluated, as
well as improved techniques for their reduction.
A possible application of this study is the development of a Supernova alarmfor Super-Kamiokande with Gadolinium. The system was designed and optimized
for the first phase of SK-Gd with 0.02% Gd2 (SO4 )3 · 8H2 O by mass, looking for
Inverse Beta Decay candidates and provide warnings in case of increasement in
event rate. The pre-Supernova alert system can give early warnings of 2.4 to 4.2
hours assuming a false positive rate of 3.2 per century for ↵ Ori, which is thenearest red giant star to Earth. For detection above 3 , the early warnings is 3.2
to 5.2. Maximum detection range for the alarm is 400 pc.
Next generation experiment, Hyper-Kamiokande, has a great potential for preSupernova detection if it will be gadolinium doped. Detection ranges are estimatedto be greater than 1 kpc for a false positive rate of 3.2 per century and an alert
system could give warnings of over 10 hours for ↵ Ori. Alternative designs forphotodetector using a hybrid combination of 20-inch PMTs and mPMT modules

Conclusion (English)212

are being studied for Hyper-K. This configuration could improve low energy reconstruction capabilities for its enhanced vertex and energy resolution, among otherbenefits.

Chapter 9

Conclusioni (Italian)

L’attuale fase sperimentale del rivelatore Super-Kamiokande SK-Gd permette dimigliorare la capacità dell’esperimento nell’identificazione di neutroni, riducendo
la soglia di energia per la rivelazione dei neutrini. E’ cosı̀ ora possibile osservare
neutrini ⌫¯e da pre-Supernova, mai rilevati prima, e realizzare un sistema di allarme
per i collassi di Supernovae.
In [16] è stata valutata la sensibilità per la rivelazione di neutrini da preSupernova in Super-Kamiokande con una concentrazione percentuale in massadello 0,2% Gd2 (SO4 )3 · 8H2 O.
I risultati riportati in questo lavoro di tesi sono basati su una dettagliata stima
della contaminazione da radioisotopi del solfato di gadolinio e una aggiornata stima

213

Conclusioni (Italian)214

dei flussi di neutrini prodotti dai reattori nelle vicinanze del rivelatore, che sonodue delle principali sorgenti di contaminazione nella rivelazione dei neutrini preSupernova. Tutte le altre sorgenti di background sono state attentamente valutate.
Questo lavoro è presentato mentre la prima fase sperimentale di SK-Gd è in fasedi commissioning. Gli eventi di background saranno meglio determinati e valutati
non appena un campione statisticamente significativo dei dati sperimentali sarà
disponibile, cosı̀ come saranno migliorate le strategie per la loro riduzione.
I risultati di questo studio costituiscono la base per lo sviluppo di un sistema
di allarme per la rivelazione multimessanger di Supernova. Il sistema di allarmesviluppato in questo lavoro di tesi è stato progettato e ottimizzato per individuare
gli eventi di decadimento beta inverso di neutrino nella prima fase di SK-Gd,
con concentrazione percentuale in massa di 0,02% Gd2 (SO4 )3 · 8H2 O. Il sistema
proposto per la rivelazione di neutrini da pre-Supernova può fornire un’allerta(early warning) stimata tra le 2,4 e le 4,2 ore precedenti l’esplosione di Supernova,
assumendo un FPR (False Positive Rate) di 3,2 per secolo per la sorgente ↵ Ori(Betelgeuse), che è la stella gigante rossa più vicina alla Terra. Per sensitività
maggiori di 3 , l’allerta è tra le 3,2 e le 5,2 ore precedenti il collasso, entro una
distanza massima è di 400 pc.
Il futuro esperimento Hyper-Kamiokande, attualmente in fase di costruzione,

Conclusioni (Italian)215

potrà migliorare ulteriormente la sensibilità nella ricostruzione di neutrini da preSupernova se sarà drogato con gadolinio. Si stima che si possano rivelare eventi diSupernova a distanze maggiori di 1 kpc. Considerando un tasso di falsi positivi di
3,2 per secolo, un sistema di allerta potrebbe fornire l’allarme con oltre 10 ore di
anticipo per il collasso di ↵-Ori. Per Hyper-K sono allo studio progetti alternativi
che propongono una combinazione ibrida di PMT da 20 pollici e moduli mPMT.
Questa configurazione potrebbe migliorare le capacità di ricostruzione di eventi dineutrino di bassa energia.

Chapter 10

Conclusão (Portuguese)

A nova fase do detector Super-Kamiokande, SK-Gd, está aprimorando as capacidades do experimento de identificar nêutrons, o que possibilita a redução do limiar
de energia para a detecção de neutrinos. No contexto deste trabalho, ⌫¯e de estrelaspré-Supernova, que nunca foram detectados, são possı́veis de serem observados e
sua detecção pode fornecer um alerta precoce para um potencial colapso do núcleo
de Supernovas.
Em um trabalho anterior [16] foi avaliada a sensibilidade para a detecção deneutrinos pré-Supernova no Super-Kamiokande com 0,2% Gd2 (SO4 )3 · 8H2 O. Em
2020, uma concentração mais baixa do composto foi diluı́da no detector. Desdeentão, novos requisitos para contaminação por radioisótopos do sulfato de gadolı́nio
foram estabelecidos, bem como informações atualizadas sobre fluxos de reatores,216

Conclusão (Portuguese)217

que juntos são dois dos principais backgrounds para a detecção de neutrinos préSupernova. Todas as fontes de background precisam ser avaliadas cuidadosamente,pois a detecção de neutrinos pré-Supernova está abaixo dos limites de energia
usuais de Super-Kamiokande. No momento da redação desta tese, o primeiro
estágio do SK-Gd ainda está em fase de comissionamento e, após a aquisiçãode uma boa quantidade de dados, os backgrounds serão melhor determinados eavaliados, bem como técnicas aprimoradas para sua redução.
Uma possı́vel aplicação deste estudo é o desenvolvimento de um alarme para
Supernovas para o Super-Kamiokande com Gadolı́nio. O sistema de alerta foi
projetado e otimizado para a primeira fase do SK-Gd com 0,02% Gd2 (SO4 )3 ·8H2 O,
procurando candidatos de Decaimento Beta Inverso e fornecendo avisos em caso deum aumento na taxa de eventos esperada. O sistema de alerta de pré-Supernova
pode dar avisos antecipados de 2,4 a 4,2 horas, assumindo uma taxa de falsos
positivos de 3,2 por século para ↵ Ori, que é a estrela gigante vermelha maispróxima da Terra. Para detecções acima de 3 , os avisos são de 3,2 a 5,2. Ointervalo máximo de detecção do alarme é 400 pc.
O futuro experimento, Hyper-Kamiokande, tem um grande potencial para detecção pré-Supernova se a água do detector também for for dopada com gadolı́nio.
Os intervalos de detecção são estimados em mais de 1 kpc, para uma taxa de falsos positivos de 3,2 por século, e um sistema de alerta poderia dar avisos de maisAppendices218

de 10 horas para ↵ Ori. Projetos alternativos para fotodetectores usando uma
combinação hı́brida de PMTs de 20 polegadas e módulos mPMT estão sendo estudados para Hyper-K. Esta configuração hı́brida pode aprimorar as capacidadesde reconstrução de eventos de baixa energia, devido às melhorias nas resoluçoõesde vértice e de energia, entre outros benefı́cios.

Appendix A

Nearby pre-Supernova
Candidates
Catalog Name

Common Name

Constellation

Distance (kpc)

Mass (M )HD 116658

Spica/↵ Virginis

Virgo

0.077 ± 0.004

11.43+1.15
1.15

HD 149757⇣ Ophiuchi

Ophiuchus

0.112 ± 0.002

20.0

HD 129056↵ LupiLupus

0.143 ± 0.003

10.1+1.0
1.0

HD 78647

VelorumVela

0.167 ± 0.003

7.0+1.5
1.0

Scorpius

0.169 ± 0.030

Pegasus0.211 ± 0.006

11.7+0.8
0.8

HD 148478
HD 206778

Antares/↵ Scorpii✏ Pegasi

11.0

14.3

HD 39801

Betelgeuse/↵ Orionis

Orion

0.222 ± 0.040

11.6+5.0
3.9

HD 89388

q Car/V337 Car

Carina0.230 ± 0.020

6.9+0.6
0.6

219

Nearby pre-Supernova Candidates

220

HD 210745⇣ Cephei

Cepheus0.256 ± 0.006

10.1+0.1
0.1

HD 34085

Rigel/ Orion

Orion

0.264 ± 0.024

21.0+3.0
3.0

HD 200905

xi Cygni

Cygnus

0.278 ± 0.029

8.0

HD 47839

S Monocerotis A

Monoceros

0.282 ± 0.040

29.1

HD 47839

S Monocerotis B

Monoceros

0.282 ± 0.040

21.3

HD 93070

w Car/V520 Car

Carina0.294 ± 0.023

7.9+0.1
0.1

HD 68553

NS Puppis

Puppis0.321 ± 0.032

9.7

HD 36389

CE Tauri/119 Tauri

Taurus

0.326 ± 0.070

14.37+2.00
2.77

Vela

0.342 ± 0.035

9.0+0.6
0.6

HD 68273

2

Velorum

HD 50877

ø1 Canis Majoris

Canis Major

0.394 ± 0.052

7.83+2.0
2.0

HD 207089

12 Pegasi

Pegasus0.415 ± 0.031

6.3+0.7
0.7

HD 213310

5 Lacertae

Lacerta

0.505 ± 0.046

5.11+0.18
0.18

HD 52877

Canis Majoris

Canis Major

0.513 ± 0.108

12.3+0.1
0.1

HD 208816

VV Cephei

Cepheus0.599 ± 0.083

10.6+1.0
1.0

HD 196725✓ Delphini

Delphinus

0.629 ± 0.029

5.60+3.0
3.0

HD 203338

V381 Cephei

Cepheus0.631 ± 0.086

12.0

HD 216946

V424 Lacertae

Lacerta

0.634 ± 0.075

6.8+1.0
1.0

HD 17958

HR 861

Cassiopeia

0.639 ± 0.039

9.2+0.5
0.5

HD 80108

HR 3692

Vela0.650 ± 0.061

12.1+0.2
0.2

HD 56577

145 Canis Major

Canis Major

0.697 ± 0.078

7.8+0.5
0.5

HD 219978

V809 Cassiopeia

Cassiopeia

0.730 ± 0.074

8.3+0.5
0.5

Nearby pre-Supernova Candidates

221

HD 205349

HR 8248

Cygnus0.746 ± 0.039

6.3+0.7
0.7

HD 102098

Deneb/↵ Cygni

Cygnus0.802 ± 0.066

19.0+4.0
4.0

Table A.1: List of pre-Supernova candidates with estimated masses and distances. Reproduced from [53].

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