arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2406.12901physics.ins-detcs.LGhep-exphysics.data-an
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大型液体闪烁体探测器中电子反中微子选择的机器学习可解释方法

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

Università degli Studi di Padova(帕多瓦大学);INFN(意大利国家核物理研究院);Università degli Studi di Catania(卡塔尼亚大学)
A. Gavrikov, V. Cerrone, A. Serafini, R. Brugnera, A. Garfagnini, M. Grassi, B. Jelmini, L. Lastrucci, S. Aiello, G. Andronico, V. Antonelli, A. Barresi, D. Bas… 展开作者

A. Gavrikov, V. Cerrone, A. Serafini, R. Brugnera, A. Garfagnini, M. Grassi, B. Jelmini, L. Lastrucci, S. Aiello, G. Andronico, V. Antonelli, A. Barresi, D. Basilico, M. Beretta, A. Bergnoli, M. Borghesi, A. Brigatti, R. Bruno, A. Budano, B. Caccianiga, A. Cammi, R. Caruso, D. Chiesa, C. Clementi, S. Dusini, A. Fabbri, G. Felici, F. Ferraro, M. G. Giammarchi, N. Giudice, R. M. Guizzetti, N. Guardone, C. Landini, I. Lippi, S. Loffredo, L. Loi, P. Lombardi, C. Lombardo, F. Mantovani, S. M. Mari, A. Martini, L. Miramonti, M. Montuschi, M. Nastasi, D. Orestano, F. Ortica, A. Paoloni, E. Percalli, F. Petrucci, E. Previtali, G. Ranucci, A. C. Re, M. Redchuck, B. Ricci, A. Romani, P. Saggese, G. Sava, C. Sirignano, M. Sisti, L. Stanco, E. Stanescu Farilla, V. Strati, M. D. C. Torri, A. Triossi, C. Tuvé, C. Venettacci, G. Verde, L. Votano

AI总结:

本文提出用全连接神经网络提升大型液体闪烁体探测器(以JUNO为例)中反应堆反中微子事例选择效率,并首次通过可解释性分析揭示模型决策依据,为改进传统选择方法提供指导。

摘要:

多个中微子探测器,包括KamLAND、大亚湾、Double Chooz、RENO以及即将建成的大型JUNO,都依赖液体闪烁体来探测反应堆反中微子相互作用。在这一背景下,反β衰变是反中微子探测的黄金通道,它提供一对关联事件,从而构成区分信号与多种本底的强实验特征。然而,由于反中微子相互作用的截面很低,开发一种强大的事例选择算法对于实现信号与本底的有效区分变得至关重要。在本研究中,我们引入一种机器学习(ML)模型来实现这一目标:一个全连接神经网络,作为大型液体闪烁体探测器的强大信号-本底鉴别器。我们以JUNO探测器为例证明,尽管基于截断的方法已经具有很高的效率,所提出的ML模型仍能进一步提高整体事例选择效率。此外,它还能保留探测器边缘处的信号事例,而这些事例在该区域本底事例数量过多的情况下原本会被排除。我们还首次对反应堆中微子实验事例选择的ML方法进行了可解释性分析。该方法能够深入理解模型的决策过程,并为改进和更新传统事例选择方法提供有价值的信息。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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