Paris workshop on Bayesian Deep Learning for Cosmology and Time Domain Astrophysics

Background Estimation in Fermi Gamma-ray Burst Monitor lightcurves through a Neural Network

poster_s2_4

22 juin 2022, 11:10

2m

Buffon Amphitheater (APC laboratory, Université Paris Cité)

Poster + lightning talk Time Domain Astrophysics Lightning talks

Orateur

Dr Riccardo Crupi (Università di Udine)

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 not previously detected by other approaches.
We show the potential 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.

Documents de présentation

Crupi_Poster.pdf

Crupi_Slide.pdf