论文标题
与深层神经网络的水切伦科夫事件的最大似然重建
Maximum likelihood reconstruction of water Cherenkov events with deep generative neural networks
论文作者
论文摘要
大型水切伦科夫探测器已经塑造了我们目前对中微子物理和核子衰减的了解，并将在可预见的将来继续这样做。这些高功能的检测器允许从其光传感器上的信号中提取方向性和拓扑以及量热量信息。水切伦科夫重建的当前最新方法取决于最大样子的估计，并采用了几种简化的假设来解决问题。在本文中，我们描述了在每个光电传感器上产生信号的概率密度函数的神经网络，鉴于一组表征了检测器中粒子的输入。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，假设的假设明显较少，因此预计将改善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.
