Maximum Likelihood Reconstruction of Water Cherenkov Events With Deep Generative Neural Networks.
Maximum Likelihood Reconstruction of Water Cherenkov Events With Deep Generative Neural Networks.
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DOI：
10.3389/fdata.2022.868333
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发表时间：
2022
影响因子：
3.1
通讯作者：
Yang, Karan
中科院分区：
文献类型：
--
作者：
Jia, Mo;Kumar, Karan;Mackey, Liam S.;Putra, Alexander;Vilela, Cristovao;Wilking, Michael J.;Xia, Junjie;Yanagisawa, Chiaki;Yang, Karan
关键词：
大型水切伦科夫探测器塑造了我们目前对中微子物理和核子衰变的认识，并将在可预见的未来继续这样做。这些功能强大的探测器可以从光电传感器上的信号中提取方向、拓扑以及热量信息。目前最先进的水切伦科夫重建方法依赖于最大似然估计，并采用几个简化的假设来使问题易于处理。在本文中，我们描述了神经网络，该神经网络在给定一组表征探测器中粒子特征的输入的情况下，为每个光电传感器的信号生成概率密度函数。我们提出的神经网络允许基于可能性的事件重建方法，与传统方法相比，假设要少得多，因此有望提高水切伦科夫探测器的当前性能。
Large water Cherenkov detectors have shaped our current knowledge of neutrino physics and nucleon decay, and will continue to do so in the foreseeable future. These highly capable detectors allow for directional and topological, as well as calorimetric information to be extracted from signals on their photosensors. The current state-of-the-art approach to water Cherenkov reconstruction relies on maximum-likelihood estimation, with several simplifying assumptions employed to make the problem tractable. In this paper, we describe neural networks that produce probability density functions for the signals at each photosensor, given a set of inputs that characterizes a particle in the detector. The neural networks we propose allow for likelihood-based approaches to event reconstruction with significantly fewer assumptions compared to traditional methods, and are thus expected to improve on the current performance of water Cherenkov detectors.
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DOI：
10.1016/s0168-9002(97)00048-x
发表时间：
1997-04-11
影响因子：
1.4
作者：
Brun, R;Rademakers, F
通讯作者：
Rademakers, F
影响因子：
1.3
作者：
Aurisano, A.;Radovic, A.;Vahle, P.
通讯作者：
Vahle, P.
影响因子：
8.6
作者：
HIRATA, K;KAJITA, T;CORTEZ, BG
通讯作者：
CORTEZ, BG
DOI：
10.1098/rsta.1933.0009
发表时间：
1933-03-01
影响因子：
--
作者：
Neyman, J
通讯作者：
Neyman, J
影响因子：
5
作者：
Abratenko, P.;Alrashed, M.;Zhang, C.
通讯作者：
Zhang, C.