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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