发表机构
CNRS（法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过灵敏度曲线评估深度学习框架GammaLearn在LST-1望远镜上的性能，并与标准分析对比，应用于真实观测数据，验证其在低能段重建粒子属性的优势。
AI 中文摘要
切伦科夫望远镜阵列天文台（CTAO）标志着下一代成像大气切伦科夫望远镜（IACTs）的到来，其灵敏度相比现有仪器提升了最多10倍。它的第一个原型——大尺寸望远镜（LST-1）——已在西班牙拉帕尔玛岛的穆查乔斯罗克天文台投入运行。深度学习方法在利用模拟数据重建入射粒子关键属性（如能量、到达方向和类型）方面已展现出显著潜力。与传统依赖简化图像形状参数的方法不同，深度学习能够利用记录事件的完整时间和电荷信息，从而提供增强的性能，尤其是在LST-1可触及的低能量（约20 GeV）范围内。这一能力对于观测活动星系核等遥远河外源尤为宝贵，这些源是探索基础物理和宇宙学的关键。在本工作中，通过生成灵敏度曲线，我们评估了专为IACT数据分析设计的深度学习框架GammaLearn的性能，将其与LST-1使用的标准分析方法进行比较，并将其应用于LST-1的真实观测数据。
英文摘要
The Cherenkov Telescope Array Observatory (CTAO) marks the next generation of Imaging Atmospheric Cherenkov Telescopes (IACTs), offering a sensitivity improvement of up to a factor of 10 over current instruments. Its first prototype, the Large-Sized Telescope (LST-1), is already in operation at the Roque de los Muchachos Observatory in La Palma, Spain. Deep learning methods have shown significant promise in reconstructing key properties of incident particles, such as energy, arrival direction, and type, using simulated data. Unlike traditional approaches that rely on simplified image shape parameters, deep learning can exploit the full temporal and charge information of the recorded events, providing enhanced performance, particularly at low energies (~20 GeV) accessible by LST-1. This capability is especially valuable for observing distant extragalactic sources like Active Galactic Nuclei, which are key to probing fundamental physics and cosmology. In this work, by producing sensitivity curves, we evaluate the performance of GammaLearn, a deep learning framework tailored for IACT data analysis, by comparing it to the standard analysis used with LST-1 and applying it to real observational data from LST-1.
