发表机构
Yokohama National University; Nihon University; Kanagawa University; University of Tokyo（横滨国立大学; 日本大学; 神奈川大学; 东京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对西藏-III空气簇射阵列的伽马射线到达方向重建，提出将卷积神经网络与传统方法结合，将角分辨率提高约10%-15%，等效于增加150-200个探测器。
AI 中文摘要
西藏ASγ实验利用由塑料闪烁体组成的地面空气簇射探测器阵列（西藏-III）观测从几太电子伏特到拍电子伏特范围内的宇宙伽马射线。到达方向通常通过将簇射前沿拟合到探测器命中时间数据来重建，由此得到的角分辨率在10至100太电子伏特能量范围内约为0.5°至0.2°。在本研究中，为了进一步提高方向重建精度，我们开发了一种新的到达方向重建方法，该方法将卷积神经网络（CNN）与传统方法相结合。使用蒙特卡洛模拟生成的伽马射线事件进行评估，结果表明，与传统方法相比，角分辨率提高了约10%至15%。此外，这种性能提升在高达40°的天顶角范围内几乎没有依赖性。所提出的方法提供的性能增益相当于将包含约600个探测器的西藏-III阵列增加约150至200个探测器。
英文摘要
The Tibet AS$γ$ experiment observes cosmic gamma rays from several teraelectron volts to the petaelectron volt range using an array of ground-based surface air-shower detectors (Tibet-III), which is composed of plastic scintillators. The arrival direction is conventionally reconstructed by fitting the shower front to detector hit-timing data, and the resulting angular resolution is approximately 0.5$^\circ$ to 0.2$^\circ$ in the energy range from 10 to 100 TeV. In this study, to further improve the directional reconstruction accuracy, we developed a new arrival-direction reconstruction method that integrates a convolutional neural network (CNN) with the conventional method. Evaluations using gamma-ray events generated by Monte Carlo simulations show that the angular resolution is improved by approximately 10\,\%--15\,\% compared with that achieved with the conventional method. In addition, this performance improvement shows little dependence on the zenith angle up to 40$^\circ$. The proposed method provides a performance gain equivalent to increasing the Tibet-III array, which consists of approximately 600 detectors, by roughly 150 to 200 detectors.
CommentsSubmitted to Experimental Astronomy
