Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector

Several neutrino detectors, KamLAND, Daya Bay, Double Chooz, RENO, and the forthcoming large-scale JUNO, rely on liquid scintillator to detect reactor antineutrino interactions. In this context, inverse beta decay represents the golden channel for antineutrino detection, providing a pair of correlated events, thus a strong experimental signature to distinguish the signal from a variety of backgrounds. However, given the low cross-section of antineutrino interactions, the development of a powerful event selection algorithm becomes imperative to achieve effective discrimination between signal and backgrounds. In this study, we introduce a machine learning (ML) model to achieve this goal: a fully connected neural network as a powerful signal-background discriminator for a large liquid scintillator detector. We demonstrate, using the JUNO detector as an example, that, despite the already high efficiency of a cut-based approach, the presented ML model can further improve the overall event selection efficiency. Moreover, it allows for the retention of signal events at the detector edges that would otherwise be rejected because of the overwhelming amount of background events in that region. We also present the first interpretable analysis of the ML approach for event selection in reactor neutrino experiments. This method provides insights into the decision-making process of the model and offers valuable information for improving and updating traditional event selection approaches.

Authors

Institutions

Publication Details

Journal
Physics Letters B
Published
2024-11-19
DOI
https://doi.org/10.1016/j.physletb.2024.139141
Citations
3
Primary Topic
Neutrino Physics Research
Type
article
Field-Weighted Citation Impact
1.98

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector

L. Votano, F. Ferraro, M. Beretta, M.G. Giammarchi et al.
3 citations
Physics Letters B
Neutrino Physics Research
1.98
article

Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector

L. Votano, F. Ferraro, M. Beretta, M.G. Giammarchi, Riccardo Bruno, C. Sirignano, Andrea Triossi, Andrea Serafini, D. Orestano, A. Romani, A. Martini, M. Sisti, G. Verde, R. Caruso, Arsenii Gavrikov, F. Petrucci, A. Re, Giuseppe Andronico, L. Miramonti, E. Previtali, V. Antonelli, Rosa Maria Guizzetti, G. Ranucci, Fabio Mantovani, Andrea Barresi, M. Grassi, G. Sava, Nunzio Guardone, Beatrice Jelmini, Carlo Venettacci, R. Brugnera, D. Basilico, Vanessa Cerrone, Andrea Fabbri, I. Lippi, S. Dusini, M. Montuschi, Virginia Strati, Catia Clementi, Marco Danilo Claudio Torri, Antonio Bergnoli, F. Ortica, A. Paoloni, Lorenzo Loi, Claudio Lombardo, A. Budano, A. Garfagnini, M. Redchuck, Antonio Cammi, Lorenzo Lastrucci, Cecilia Landini, A. Brigatti, P. Lombardi, M. Borghesi, P. Saggese, S. Aiello, N. Giugice, E. Percalli, E. Stanescu Farilla, C. Tuvé, D. Chiesa, S. Loffredo, B. Ricci, B. Caccianiga, M. Nastasi, G. Felici, S. M. Mari, L. Stanco
article en
3 citations

Abstract

Several neutrino detectors, KamLAND, Daya Bay, Double Chooz, RENO, and the forthcoming large-scale JUNO, rely on liquid scintillator to detect reactor antineutrino interactions. In this context, inverse beta decay represents the golden channel for antineutrino detection, providing a pair of correlated events, thus a strong experimental signature to distinguish the signal from a variety of backgrounds. However, given the low cross-section of antineutrino interactions, the development of a powerful event selection algorithm becomes imperative to achieve effective discrimination between signal and backgrounds. In this study, we introduce a machine learning (ML) model to achieve this goal: a fully connected neural network as a powerful signal-background discriminator for a large liquid scintillator detector. We demonstrate, using the JUNO detector as an example, that, despite the already high efficiency of a cut-based approach, the presented ML model can further improve the overall event selection efficiency. Moreover, it allows for the retention of signal events at the detector edges that would otherwise be rejected because of the overwhelming amount of background events in that region. We also present the first interpretable analysis of the ML approach for event selection in reactor neutrino experiments. This method provides insights into the decision-making process of the model and offers valuable information for improving and updating traditional event selection approaches.

Physics Letters BVol. 860
Roma Tre University (IT), University of Padua (IT), University of Milan (IT), Istituto Nazionale di Fisica Nucleare, Sezione di Padova (IT), Istituto Nazionale di Fisica Nucleare, Sezione di Catania (IT), Istituto Nazionale di Fisica Nucleare, Sezione di Milano (IT), Istituto Nazionale di Fisica Nucleare, Sezione di Milano Bicocca (IT)
European Commission, Horizon 2020, H2020 Marie Skłodowska-Curie Actions
Affordable and clean energy
Openalex Percentile: Top 8%
Neutrino Physics Research
1.98
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.