论文标题
在下一个实验中使用深卷积神经网络的背景拒绝演示
Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
论文作者
论文摘要
卷积神经网络（CNN）是广泛使用的最先进的计算机视觉工具，在高能量物理学中变得越来越流行。在本文中，我们试图在下一个实验中了解CNN对事件分类的潜力，该实验将在$^{136} $ XE中搜索中微子双β衰变。为此，我们演示了CNN用于鉴定电子峰值生产事件的使用情况，该事件表现出类似于中微子双β衰变事件的拓扑结构。这些事件是在$^{228} $ th校准源中使用2.6-mev伽玛射线在下一个白色高压Xenon TPC中生产的。我们在蒙特卡洛模拟的事件上训练网络，并表明，通过应用即时数据增强，可以使网络与仿真和数据之间的差异相差。与以前的非CNN分析相比，CNN的使用可显着改善信号效率/背景排斥。
Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in $^{136}$Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6-MeV gamma rays from a $^{228}$Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offer significant improvement in signal efficiency/background rejection when compared to previous non-CNN-based analyses.
