This thesis comprises the first three chapters dedicated to providing an overview of Gamma Ray-Bursts (GRBs), their properties, the instrumentation used to detect them, and Artificial Intelligence (AI) applications in the context of GRBs, including a literature review and future prospects. Considering both the current and the next generation of high X-ray monitors, such as Fermi-GBM and HERMES Pathfinder (an in-orbit demonstration of six 3U nano-satellites), the research question revolves around the detection of long and faint high-energy transients, potentially GRBs, that might have been missed by previous detection algorithms. To address this, two chapters introduce a new data-driven framework, DeepGRB. In Chapter 4, a Neural Network (NN) is described for background count rate estimation for X/gamma-ray detectors, providing a performance evaluation in different periods, including both solar maxima, solar minima periods, and one containing an ultra-long GRB. The application of eXplainable Artificial Intelligence (XAI) is performed for global and local feature importance analysis to better understand the behavior of the NN. Chapter 5 employs FOCuS-Poisson for anomaly detection in count rate observations and estimation from the NN. DeepGRB demonstrates its capability to process Fermi-GBM data, confirming cataloged events and identifying new ones, providing further analysis with estimates for localization, duration, and classification. The chapter concludes with an automated classification method using Machine Learning techniques that incorporates XAI for eventual bias identification.

该论文主要介绍天文学中伽马射线暴（GRBs）的概述、检测仪器、人工智能在GRBs的应用，包括文献综述和未来前景；通过引入新的数据驱动框架DeepGRB，介绍了神经网络在高能探测器中背景计数率估计的方法，以及FOCuS-Poisson算法在异常检测中的应用。通过DeepGRB处理数据，证实了目录事件并发现新事件，提供了定位、持续时间和分类的估计分析，并采用机器学习技术进行自动分类方法，结合可解释的人工智能以识别潜在偏见。

深度学习用于伽玛射线暴：空间望远镜 X/伽玛射线分析的数据驱动事件框架