论文标题
研究一种深度学习方法来分析来自多个伽马射线望远镜的图像
Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes
论文作者
论文摘要
成像大气Cherenkov望远镜（IACT）阵列记录来自伽玛射线发起的空气淋浴的图像，进入大气中，从而可以在非常高的能量下观察到天体物理来源。为了最大程度地提高IACT灵敏度，必须使用来自多个望远镜的图像有效地将伽马射线阵雨与宇宙射线阵雨的主要背景区分开。已经提出了卷积神经网络（CNN）与复发性神经网络（RNN）的组合来执行此任务。使用CTLEARN，使用深度学习的开源Python软件包来分析来自IACTS的数据，并使用即将到来的Cherenkov望远镜阵列（CTA）中的模拟数据，我们实现了CNN-RNN网络，没有找到证据表明按幅度拒绝绩效将望远镜图像分类。
Imaging atmospheric Cherenkov telescope (IACT) arrays record images from air showers initiated by gamma rays entering the atmosphere, allowing astrophysical sources to be observed at very high energies. To maximize IACT sensitivity, gamma-ray showers must be efficiently distinguished from the dominant background of cosmic-ray showers using images from multiple telescopes. A combination of convolutional neural networks (CNNs) with a recurrent neural network (RNN) has been proposed to perform this task. Using CTLearn, an open source Python package using deep learning to analyze data from IACTs, with simulated data from the upcoming Cherenkov Telescope Array (CTA), we implement a CNN-RNN network and find no evidence that sorting telescope images by total amplitude improves background rejection performance.
