Speaker
Description
The aim of this work is to provide a data-driven approach to estimate a background model for the Gamma-Ray Burst Monitor (GBM) of Fermi satellite. We employ a Neural Network (NN) to estimate each detector background signal given the information of the satellite: position, velocity, direction of the detectors, etc.
The estimated background can be employed into a triggering algorithm to discover significant long/weak events that are and previously not detected by other approaches.
We show the potentiality of the model by estimating the background on GBM data for Gamma-Ray Bursts (GRBs) present in GBM cataloge, the long GRB 190320 and ultra-long GRB 091024.
The proposed approach is straightforwardly generalizable to estimate the background model of other satellites.
| Main Topic | Time series analysis, transients |
|---|---|
| Secondary Topic | Classification and regression |
| Participation mode | Remote |
