We employ neural networks for classification of data of the TUS fluorescence telescope, the world’s first orbital detector of ultra-high energy cosmic rays. We focus on two particular types of signals in the TUS data: track-like flashes produced by cosmic ray hits of the photodetector and flashes that originated from distant lightnings. We demonstrate that even simple neural networks combined with certain conventional methods of data analysis can be highly effective in tasks of classification of data of fluorescence telescopes.

中文翻译：

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神经网络在TUS轨道望远镜数据分类中的应用

我们采用神经网络对 TUS 荧光望远镜的数据进行分类，这是世界上第一个超高能宇宙射线轨道探测器。我们关注 TUS 数据中两种特定类型的信号：由宇宙射线撞击光电探测器产生的轨道状闪光和来自遥远闪电的闪光。我们证明，即使是简单的神经网络与某些传统的数据分析方法相结合，也可以在荧光望远镜数据分类任务中非常有效。