arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2202.01276hep-ex
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基于深度生成神经网络的水切伦科夫事件极大似然重建

Maximum likelihood reconstruction of water Cherenkov events with deep generative neural networks

Mo Jia, Karan Kumar, Liam S. Mackey, Alexander Putra, Cristovao Vilela, Michael J. Wilking, Junjie Xia, Chiaki Yanagisawa, Karan Yang

摘要:

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知识,并将在可预见的未来继续这样做。这些高性能的探测器允许从其光电传感器上的信号中提取方向和拓扑以及量热信息。目前最先进的水切伦科夫重建方法依赖于最大似然估计,采用几个简化的假设,使问题易于处理。在本文中,我们描述了神经网络,在每个光电传感器的信号产生概率密度函数,给定一组输入,在检测器中的粒子的特征。与传统方法相比,我们提出的神经网络允许基于似然的事件重建方法,具有更少的假设,因此有望提高水切伦科夫探测器的当前性能。

英文摘要:

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.

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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