使用深度学习和CTLearn重建立体CTA事件
Reconstruction of stereoscopic CTA events using deep learning with CTLearn
摘要：
Cherenkov望远镜阵列（CTA）是一个由数十个成像大气Cherenkov望远镜（IACT）组成的阵列，是下一代地基伽马射线天文台的国际项目，旨在将当前一代仪器的灵敏度提高五到十倍，并提供从20 GeV到300 TeV以上的能量覆盖。IACT阵列探测极高能伽马射线天空。它们的工作原理是同时观测由甚高能伽马射线和宇宙射线与大气相互作用引发的空气簇射。由给定簇射引起的切伦科夫光子被聚焦到阵列中望远镜的相机平面上，产生事件的多立体记录。这幅图像包含了空气簇射的纵向发展，以及它的空间、时间和热量信息。原始极高能量粒子的属性（类型，能量和入射方向）可以通过使用机器学习技术重建整个事件来从这些图像中推断。在这篇文章中，我们使用CTLearn对模拟的立体IACT事件进行了纯深度学习驱动的全事件重建。CTLearn是一个软件包，其中包括用于加载和操作IACT数据以及运行深度学习模型的模块，使用像素相机数据作为输入。
英文摘要：
The Cherenkov Telescope Array (CTA), conceived as an array of tens of imaging atmospheric Cherenkov telescopes (IACTs), is an international project for a next-generation ground-based gamma-ray observatory, aiming to improve on the sensitivity of current-generation instruments a factor of five to ten and provide energy coverage from 20 GeV to more than 300 TeV. Arrays of IACTs probe the very-high-energy gamma-ray sky. Their working principle consists of the simultaneous observation of air showers initiated by the interaction of very-high-energy gamma rays and cosmic rays with the atmosphere. Cherenkov photons induced by a given shower are focused onto the camera plane of the telescopes in the array, producing a multi-stereoscopic record of the event. This image contains the longitudinal development of the air shower, together with its spatial, temporal, and calorimetric information. The properties of the originating very-high-energy particle (type, energy, and incoming direction) can be inferred from those images by reconstructing the full event using machine learning techniques. In this contribution, we present a purely deep-learning driven, full-event reconstruction of simulated, stereoscopic IACT events using CTLearn. CTLearn is a package that includes modules for loading and manipulating IACT data and for running deep learning models, using pixel-wise camera data as input.
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。
