用神经网络提升高能伽马射线探测精度，显著增强弱信号识别能力。
Gamma/hadron separation in the TAIGA experiment with neural network methods
- 采用卷积神经网络直接处理切伦科夫望远镜图像，替代传统希尔拉斯参数分析。
- 在21小时观测中实现超过5.5σ的伽马信号显著性，优于传统方法。
- 适用于高背景噪声下稀有伽马源探测，适合高能天体物理研究者。
本文研究了在宇宙射线通量远高于点源伽马射线通量（比例高达10⁴）的情况下，利用神经网络方法筛选稀有甚高能伽马射线的能力。该条件适用于TeV能区的蟹状星云，其为伽马天文学中广泛使用的校准与方法测试标准源。TAIGA实验中的三台成像大气切伦科夫望远镜也观测该源。切伦科夫望远镜获取广延空气簇射图像，传统方法使用希尔拉斯参数分析，也可直接通过卷积神经网络处理图像。本工作描述了基于神经网络进行伽马/强子分离的主要步骤与结果，并与TAIGA协作组采用的传统处理方法及希尔拉斯参数截断法进行比较。结果显示，经神经网络处理后，在21小时的蟹状星云观测中获得高于5.5σ的信号显著性。
原文摘要 · Abstract (English)
In this work, the ability of rare VHE gamma ray selection with neural network methods is investigated in the case when cosmic radiation flux strongly prevails (ratio up to {10^4} over the gamma radiation flux from a point source). This ratio is valid for the Crab Nebula in the TeV energy range, since the Crab is a well-studied source for calibration and test of various methods and installations in gamma astronomy. The part of TAIGA experiment which includes three Imaging Atmospheric Cherenkov Telescopes observes this gamma-source too. Cherenkov telescopes obtain images of Extensive Air Showers. Hillas parameters can be used to analyse images in standard processing method, or images can be processed with convolutional neural networks. In this work we would like to describe the main steps and results obtained in the gamma/hadron separation task from the Crab Nebula with neural network methods. The results obtained are compared with standard processing method applied in the TAIGA collaboration and using Hillas parameter cuts. It is demonstrated that a signal was received at the level of higher than 5.5σ in 21 hours of Crab Nebula observations after processing the experimental data with the neural network method.
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