Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA-IACT telescope has 560 pixels of hexagonal structure. Images in such cameras can be analysed by deep learning techniques to extract numerous physical and geometrical parameters and/or for incoming particle identification. The most powerful deep learning technique for image analysis, the so-called convolutional neural network (CNN), was implemented in this study. Two open source libraries for machine learning, PyTorch and TensorFlow, were tested as possible software platforms for particle identification in imaging air Cherenkov telescopes. Monte Carlo simulation was performed to analyse images of gamma-rays and background particles (protons) as well as estimate identification accuracy. Further steps of implementation and improvement of this technique are discussed.
翻译:现代宇宙伽马射线探测器是一种特殊类型的成像望远镜(Cherenkov望远镜),配备了数量相对较多的光倍像像素照相机,例如,TAIGA-IACT望远镜的照相机有560像素六边形结构,可通过深层学习技术分析这些照相机中的图像,以提取许多物理和几何参数和(或)进入的粒子识别。在本研究中采用了最强大的图像分析深层次学习技术,即所谓的脉冲神经网络(CNN),对两个用于机器学习的开放源库PyTorch和TensorFlow进行了测试,作为在成像空气切伦科夫望远镜中进行微粒识别的可能软件平台。进行了蒙特卡洛模拟,以分析伽马射线和背景粒子(质谱)的图像,并估计识别准确性。讨论了实施和改进这一技术的进一步步骤。