AI 中文总结
研究针对切伦科夫望远镜阵列天文台，采用基于事件类型的分析方法，通过训练神经网络划分事件类型并独立计算仪器响应函数，经模拟观测和分析，提升了空间分辨率和灵敏度，对CTAO数据科学利用意义重大。
AI 中文摘要
成像大气切伦科夫望远镜传统分析方法是优化质量切割以选择高质量事件子样本，用单一仪器响应函数（IRF）进行数据科学解释，其余事件被丢弃。费米大面积望远镜等实验采用的基于事件类型的分析方法，将数据集分为子样本，为每个子样本独立计算IRF。本文用模拟数据对未来切伦科夫望远镜阵列天文台（CTAO）进行基于事件类型分析的概念验证。训练神经网络预测事件方向重建误差，据此划分事件类型，计算各事件类型的IRF并与标准分析的比较。用这些IRF模拟观测并用高级分析工具分析，结果显示空间分辨率提高25%至50%，灵敏度提高约25%，对CTAO数据科学利用有重要意义。
英文摘要
The analysis traditionally employed by Imaging Atmospheric Cherenkov Telescopes involves optimizing quality cuts to select a sub-sample of high-quality events. These events are used for the scientific interpretation of the data, employing a single set of Instrument Response Functions (IRFs). All selected events are treated equally and assumed to be well represented by these IRFs, while the rest are discarded. An alternative approach, successfully applied in experiments such as Fermi-LAT, is an event-type-based analysis. This method divides datasets into subsamples, each containing events of a given expected reconstruction quality. IRFs are computed for each subsample independently, improving the accuracy with which IRFs represent the reconstruction quality of each event. The high-level analysis of these subsamples is performed treating them as independent observations, each with their own set of IRFs, and analyzed jointly. In this work we present a proof-of-concept implementation of an event-type-based analysis for the future CTAO using simulated data. A neural network (specifically a multi-layer perceptron) is trained to predict the direction reconstruction error of each event, and the simulated dataset is divided into event types based on this predicted variable. We compute IRFs for each event type and compare them with those from the standard analysis (without event types). Finally, we simulate observations using these event-type-wise IRFs and analyze them with high-level analysis tools to test the performance of both approaches. This implementation demonstrates notable improvements: 25% to 50% boost in spatial resolving power and ~25% in sensitivity. This boost in performance will have strong implications in the scientific exploitation of the CTAO data, especially in crowded regions such as the Galactic Plane or searching for spectral signatures like Dark Matter annihilation lines.
Comments37 pages, 12 figures
Journal refAstroparticle Physics, Volume 182, 2026, page 103227
DOI：10.1016/j.astropartphys.2026.103277
