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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
