论文标题
在Taiga实验中使用深度学习方法的Cherenkov望远镜图像分析中的能量重建
Energy Reconstruction in Analysis of Cherenkov Telescopes Images in TAIGA Experiment Using Deep Learning Methods
论文作者
论文摘要
TAIGA天体物理复合物的成像大气Cherenkov望远镜（IACT）可以观察到高能量伽马辐射有助于研究许多天体物理对象和过程。 Taiga-IACT使我们能够从总宇宙辐射通量中选择γ量子，并恢复其主要参数，例如能量和到达方向。处理所得图像的传统方法是图像参数化 - 所谓的希拉斯参数方法。目前，机器学习方法，特别是深度学习方法已积极用于IACT图像处理。本文通过几种深度学习方法（单模式）和多个IACT望远镜（立体模式）对模拟蒙特卡洛图像进行了分析。进行了能量重建质量的估计，并使用几种类型的神经网络分析了它们的能量光谱。使用开发的方法，还将获得的结果与基于希拉斯参数获得的传统方法获得的结果进行了比较。
Imaging Atmospheric Cherenkov Telescopes (IACT) of TAIGA astrophysical complex allow to observe high energy gamma radiation helping to study many astrophysical objects and processes. TAIGA-IACT enables us to select gamma quanta from the total cosmic radiation flux and recover their primary parameters, such as energy and direction of arrival. The traditional method of processing the resulting images is an image parameterization - so-called the Hillas parameters method. At the present time Machine Learning methods, in particular Deep Learning methods have become actively used for IACT image processing. This paper presents the analysis of simulated Monte Carlo images by several Deep Learning methods for a single telescope (mono-mode) and multiple IACT telescopes (stereo-mode). The estimation of the quality of energy reconstruction was carried out and their energy spectra were analyzed using several types of neural networks. Using the developed methods the obtained results were also compared with the results obtained by traditional methods based on the Hillas parameters.
