The aim of this work is to propose a Machine Learning approach to estimate a background model for the Gamma-Ray Burst Monitor (GBM) of the Fermi satellite. It is employed a Neural Network (NN) to estimate each detector background signal given the information of the satellite and its detectors. The estimated background can be employed into a triggering algorithm to discover significant long/weak events that are not detected by other approaches. It is shown the potential of the model by estimating the background on GBM data for a Gamma-Ray Burst (GRB) present in the GBM catalog, the ultra-long GRB 091024. The proposed approach is straightforwardly generalizable to estimate the background model of other satellites.

Background Estimation in Fermi Gamma-Ray Burst Monitor Lightcurves Through a Neural Network

Riccardo Crupi
Primo
2023-01-01

Abstract

The aim of this work is to propose a Machine Learning approach to estimate a background model for the Gamma-Ray Burst Monitor (GBM) of the Fermi satellite. It is employed a Neural Network (NN) to estimate each detector background signal given the information of the satellite and its detectors. The estimated background can be employed into a triggering algorithm to discover significant long/weak events that are not detected by other approaches. It is shown the potential of the model by estimating the background on GBM data for a Gamma-Ray Burst (GRB) present in the GBM catalog, the ultra-long GRB 091024. The proposed approach is straightforwardly generalizable to estimate the background model of other satellites.
2023
9783031341663
9783031341670
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11390/1264124
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