基于人工神经网络的TACTIC gamma-ray望远镜能量重建方法及其与其他传统方法的比较
ANN-based energy reconstruction procedure for TACTIC gamma-ray telescope and its comparison with other conventional methods
摘要：
不同研究小组采用的能量估算方法，包括SIZE和DISTANCE的多项式拟合、一般最小二乘拟合和基于内插的查找表等方法，用于使用单个大气切伦科夫成像望远镜确定初级$γ$射线的能量。提出了一种基于人工神经网络的TACTIC大气切伦科夫成像望远镜能量重建方法。该程序使用3：30：1的人工神经网络配置与弹性反向传播算法来估计能量的$γ$射线类事件的基础上，其图像大小，距离和天顶距。新的基于人工神经网络的能量重建方法，除了产生的能量分辨率为$\sim$ 26%，这是与其他单一的成像望远镜，具有额外的优势，它认为天顶距的依赖性以及。详细的基于人工神经网络的能量估计过程，以及其与其他传统的能量重建方法的比较性能的文件中，结果表明，在这项工作中考虑的所有方法，人工神经网络方法产生最好的结果。基于人工神经网络的能量重建的性能也得到了验证，通过确定能量范围为1-16 TeV的蟹状星云的能谱，由TACTIC望远镜测量。
英文摘要：
The energy estimation procedures employed by different groups, for determining the energy of the primary $γ$-ray using a single atmospheric Cherenkov imaging telescope, include methods like polynomial fitting in SIZE and DISTANCE, general least square fitting and look-up table based interpolation. A novel energy reconstruction procedure, based on the utilization of Artificial Neural Network (ANN), has been developed for the TACTIC atmospheric Cherenkov imaging telescope. The procedure uses a 3:30:1 ANN configuration with resilient backpropagation algorithm to estimate the energy of a $γ$-ray like event on the basis of its image SIZE, DISTANCE and zenith angle. The new ANN-based energy reconstruction method, apart from yielding an energy resolution of $\sim$ 26%, which is comparable to that of other single imaging telescopes, has the added advantage that it considers zenith angle dependence as well. Details of the ANN-based energy estimation procedure along with its comparative performance with other conventional energy reconstruction methods are presented in the paper and the results indicate that amongst all the methods considered in this work, ANN method yields the best results. The performance of the ANN-based energy reconstruction has also been validated by determining the energy spectrum of the Crab Nebula in the energy range 1-16 TeV, as measured by the TACTIC telescope.
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。
