基于DAMPE实验的电子识别神经网络分类器
A neural network classifier for electron identification on the DAMPE experiment
摘要：
暗物质粒子探测器（DAMPE）是一个星载粒子探测器和宇宙射线观测站，自2015年以来一直在运行，旨在探测能量从几个GeV到10 TeV的电子和伽马射线，以及高达100 TeV的宇宙质子和原子核。主要的科学目标之一是精确测量宇宙电子+正电子通量，由于轨道上有非常大的质子背景，这需要一种强大的粒子识别方法。在过去的十年中，机器学习领域为我们提供了所需的工具。本文提出了一种基于神经网络的宇宙电子识别和质子排斥的方法，并展示了其性能的基础上模拟蒙特卡罗数据。对于相同的检测效率，神经网络比经典的基于切割的方法达到显著更低的背景，特别是在最高能量下。模拟和真实数据之间的良好匹配完成了图片。
英文摘要：
The Dark Matter Particle Explorer (DAMPE) is a space-borne particle detector and cosmic ray observatory in operation since 2015, designed to probe electrons and gamma rays from a few GeV to 10 TeV energy, as well as cosmic protons and nuclei up to 100 TeV. Among the main scientific objectives is the precise measurement of the cosmic electron+positron flux, which due to the very large proton background in orbit requires a powerful particle identification method. In the past decade, the field of machine learning has provided us the needed tools. This paper presents a neural network based approach to cosmic electron identification and proton rejection and showcases its performances based on simulated Monte Carlo data. The neural network reaches significantly lower background than the classical, cut-based method for the same detection efficiency, especially at highest energies. A good matching between simulations and real data completes the picture.
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