深度学习与IACT：使用域适应弥合蒙特卡罗模拟与LST-1数据之间的差距
Deep Learning and IACT: Bridging the gap between Monte-Carlo simulations and LST-1 data using domain adaptation
AI总结：
本文针对深度学习模型从模拟向真实观测数据迁移时的域偏移问题，将域适应技术整合到CTAO物理分析框架中，利用LST-1真实数据验证了该方法带来的性能提升。
摘要：
切伦科夫望远镜阵列观测站（CTAO）是采用大气成像切伦科夫技术研究极高能伽马射线的下一代观测站。在模拟数据上进行入射粒子物理属性重建时，深度学习方法已展现出可喜的成果。然而，由于基于深度学习的模型易受域偏移影响，将该方法应用于观测数据面临诸多挑战。本文将域适应技术整合到CTAO基于物理的背景中，并利用LST-1真实观测数据阐明了这些技术带来的性能提升。
英文摘要：
The Cherenkov Telescope Array Observatory (CTAO) is the next generation of observatories employing the imaging air Cherenkov technique for the study of very high energy gamma rays. The deployment of deep learning methods for the reconstruction of physical attributes of incident particles has evinced promising outcomes when conducted on simulations. However, the transition of this approach to observational data is accompanied by challenges, as deep learning-based models are susceptible to domain shifts. In this paper, we integrate domain adaptation in the physics-based context of the CTAO and shed light on the gain in performance that these techniques bring using LST-1 real acquisitions.
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。
