用归一化流检测宇宙伽马射线，提升稀有事件识别能力
Capability of using the normalizing flows for extraction rare gamma events in the TAIGA experiment
- 基于深度学习与归一化流设计异常检测方法
- 在TAIGA-IACT模拟数据上实现有效识别
- 适合高能物理中稀有信号探测研究者
本工作旨在利用深度学习与基于归一化流的异常检测方法，从宇宙源粒子流中识别罕见伽马量子，以对抗带电粒子背景。结果显示该方法具备探测伽马射线的潜力。该方法在TAIGA-IACT实验的模拟数据上进行了测试，其定量性能指标目前仍低于其他现有方法，因此提出了改进实施路径的可能方向。
原文摘要 · Abstract (English)
The objective of this work is to develop a method for detecting rare gamma quanta against the background of charged particles in the fluxes from sources in the Universe with the help of the deep learning and normalizing flows based method designed for anomaly detection. It is shown that the suggested method has a potential for the gamma detection. The method was tested on model data from the TAIGA-IACT experiment. The obtained quantitative performance indicators are still inferior to other approaches, and therefore possible ways to improve the implementation of the method are proposed.
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