Three-dimensional convolutional neural networks for neutrinoless double-beta decay signal/background discrimination in high-pressure gaseous Time Projection Chamber
Three-dimensional convolutional neural networks for neutrinoless double-beta decay signal/background discrimination in high-pressure gaseous Time Projection Chamber
复制标题
高压气体时间投影室中无中微子双β衰变信号/背景辨别的三维卷积神经网络
DOI：
10.1088/1748-0221/13/08/p08015
复制
发表时间：
2018-08-01
影响因子：
1.3
通讯作者：
Sun, X.
中科院分区：
文献类型：
--
作者：
Ai, P.;Wang, D.;Sun, X.
在寻找无中微子双β衰变时，高压气体时间投影室具有明显的优势，因为粒子相互作用产生的电离电荷径迹被延长，探测器通过适当的电荷读出系统捕获完整的三维电荷分布。这些轨迹信息为区分信号事件和背景提供了重要的额外处理。在本文中，我们构建了一个玩具模型来演示辨别力来自哪里，以及神经网络模型已经利用了多少。然后，我们在模拟的双β和背景电荷轨道上调整了三维卷积和残差神经网络，并测试了它们对这两种类型事件进行分类的能力。我们表明，神经网络的3D结构和整体深度都显着提高了分类器的准确性，并导致比以前的作品更好的结果。我们还研究了它们在不同的空间粒度以及不同的扩散和噪声条件下的性能。结果表明，该方法是稳定的，以及推广，尽管不同的实验条件。
In the search for neutrinoless double-beta decay, the high-pressure gaseous Time Projection Chamber has a distinct advantage, because the ionization charge tracks produced by particle interactions are extended and the detector captures the full three-dimensional charge distribution with appropriate charge readout systems. Such information of tracks provides a crucial extra-handle for discriminating signal events against backgrounds. In this paper, we constructed a toy model to demonstrate where the discrimination power comes from and how much of it the neural network models have already harnessed. Then we adapted 3-dimensional convolutional and residual neural networks on the simulated double-beta and background charge tracks and tested their capabilities in classifying these two types of events. We show that both the 3D structure and the overall depth of the neural networks significantly improve the accuracy of the classifier and lead to results better than previous works. We also studied their performance under various spatial granularities as well as different diffusion and noise conditions. The results indicate that the methods are stable and generalize well despite varying experimental conditions.