基于人工神经网络的TACTIC望远镜γ-强子分离方法
Artificial Neural Network based gamma-hadron segregation methodology for TACTIC telescope
摘要：
切伦科夫成像望远镜的灵敏度很大程度上取决于对宇宙射线背景事件的抑制。已经用于实现来自源的伽马射线和背景宇宙射线之间的分离的方法包括超割/动态超割、极大似然分类器、核方法、分形、小波和随机森林等方法。虽然隔离潜力的神经网络分类器已在过去进行了研究，结果不多，本文的主要目的是研究伽马/强子隔离潜力的各种人工神经网络算法，其中一些应该是更强大的更好的收敛性和较低的错误相比，常用的反向传播算法。所得结果表明，Levenberg-Marquardt方法优于所有其他方法在人工神经网络域。将该神经网络算法应用于TACTIC望远镜2005年11月10日至2006年1月30日期间收集的蟹状星云数据的$\sim$ 101.44 h，产生了过量的$\sim$（1141 $\pm$ 106），统计学显著性为$\sim$ 11.07 $σ$，相对于过量的$\sim$（928 $\pm$ 100），采用动态超切选择方法获得的统计学显著性为$\sim$ 9.40 $σ$。人工神经网络方法的主要优点是，它是更有效地在更高的能量，这使我们能够重新确定蟹状星云的能量范围$\sim$ 1-24 TeV的能谱。
英文摘要：
The sensitivity of a Cherenkov imaging telescope is strongly dependent on the rejection of the cosmic-ray background events. The methods which have been used to achieve the segregation between the gamma-rays from the source and the background cosmic-rays, include methods like Supercuts/Dynamic Supercuts, Maximum likelihood classifier, Kernel methods, Fractals, Wavelets and random forest. While the segregation potential of the neural network classifier has been investigated in the past with modest results, the main purpose of this paper is to study the gamma / hadron segregation potential of various ANN algorithms, some of which are supposed to be more powerful in terms of better convergence and lower error compared to the commonly used Backpropagation algorithm. The results obtained suggest that Levenberg-Marquardt method outperforms all other methods in the ANN domain. Applying this ANN algorithm to $\sim$ 101.44 h of Crab Nebula data collected by the TACTIC telescope, during Nov. 10, 2005 - Jan. 30, 2006, yields an excess of $\sim$ (1141$\pm$106) with a statistical significance of $\sim$ 11.07$σ$, as against an excess of $\sim$ (928$\pm$100) with a statistical significance of $\sim$ 9.40$σ$ obtained with Dynamic Supercuts selection methodology. The main advantage accruing from the ANN methodology is that it is more effective at higher energies and this has allowed us to re-determine the Crab Nebula energy spectrum in the energy range $\sim$ 1-24 TeV.
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。
