Deep Learning for Gamma-Ray Bursts: A data driven event framework for X/Gamma-Ray analysis in space telescopes

• URL: http://arxiv.org/abs/2401.15632v1
• Date: Sun, 28 Jan 2024 11:49:57 GMT
• Title: Deep Learning for Gamma-Ray Bursts: A data driven event framework for X/Gamma-Ray analysis in space telescopes
• Authors: Riccardo Crupi
• Abstract summary: This thesis is dedicated to providing an overview of Gamma Ray-Bursts (GRBs), their properties, the instrumentation used to detect them, and Artificial Intelligence (AI) applications. Considering both the current and the next generation of high X-ray monitors, such as Fermi-GBM and HERMES Pathfinder, the research question revolves around the detection of long and faint high-energy transients. To address this, two chapters introduce a new data-driven framework, DeepGRB.
• Score: 2.4666310814233703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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