清理还是不清理？切伦科夫望远镜阵列天文台（CTAO）中像素移除对基于深度学习的事件重建的影响
To clean or not to clean? Influence of pixel removal on event reconstruction using deep learning in CTAO
AI总结：
本研究针对CTAO望远镜数据处理中像素移除步骤，评估其对gamma-PhysNet事件重建性能的影响，以明确该步骤在数据压缩与信息保留间的权衡。
摘要：
切伦科夫望远镜阵列天文台（CTAO）是下一代地面观测站，采用成像大气切伦科夫技术研究甚高能伽马射线。Gammalearn软件提议将深度学习作为CTAO数据分析的一部分，通过最小化预处理直接从望远镜捕获的图像中重建事件参数，以最大限度保留信息。CTAO的数据分析将包含数据量缩减步骤，该步骤必然会移除像素；此步骤对数据传输和存储至关重要，但也可能导致敏感算法（如神经网络，NN）可利用的信息丢失。本研究评估了对首台大型望远镜（Large-Sized Telescope）的蒙特卡洛模拟图像应用不同清理掩码时gamma-PhysNet的性能，旨在评估数据压缩驱动的像素移除对数据处理的影响。
英文摘要：
The Cherenkov Telescope Array Observatory (CTAO) is the next generation of ground-based observatories employing the imaging air Cherenkov technique for the study of very high energy gamma rays. The software Gammalearn proposes to apply Deep Learning as a part of the CTAO data analysis to reconstruct event parameters directly from images captured by the telescopes with minimal pre-processing to maximize the information conserved. In CTAO, the data analysis will include a data volume reduction that will definitely remove pixels. This step is necessary for data transfer and storage but could also involve information loss that could be used by sensitive algorithms such as neural networks (NN). In this work, we evaluate the performance of the gamma-PhysNet when applying different cleaning masks on images from Monte-Carlo simulations from the first Large-Sized Telescope. This study is critical to assess the impact of pixel removal in the data processing, mainly motivated by data compression.
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。
