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arXiv 2001.03602astro-ph.IM
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研究一种深度学习方法来分析来自多个Gamma-ray望远镜的图像

Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes

Aryeh Brill, Qi Feng, T. Brian Humensky, Bryan Kim, Daniel Nieto, Tjark Miener

摘要:

成像大气切伦科夫望远镜阵列记录伽马射线进入大气层引发的空气簇射的图像,从而可以在非常高的能量下观察天体物理源。为了最大限度地提高IACT的灵敏度,必须使用多个望远镜的图像有效地将伽马射线簇射与宇宙射线簇射的主要背景区分开来。已经提出了卷积神经网络(CNN)与递归神经网络(RNN)的组合来执行此任务。使用CTLearn,一个使用深度学习来分析来自IACT的数据的开源Python包,以及来自即将到来的切伦科夫望远镜阵列(CTA)的模拟数据,我们实现了一个CNN-RNN网络,并没有发现任何证据表明通过总振幅对望远镜图像进行排序可以提高背景抑制性能。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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