Source detection using a 3D sparse representation: application to the Fermi gamma-ray space telescope

TL;DR

This paper extends the multiscale variance stabilization transform (MSVST) to 3D data for detecting high-energy gamma-ray sources, enabling fast, efficient source detection and characterization in Fermi LAT observations.

Contribution

The paper introduces a 3D extension of MSVST for Poisson data, tailored for astrophysical source detection in gamma-ray observations, improving speed and spectral estimation capabilities.

Findings

Effective detection of gamma-ray sources in simulated Fermi LAT data.

Fast processing compared to traditional likelihood fitting methods.

Potential for real-time characterization of variable astrophysical sources.

Abstract

The multiscale variance stabilization Transform (MSVST) has recently been proposed for Poisson data denoising. This procedure, which is nonparametric, is based on thresholding wavelet coefficients. We present in this paper an extension of the MSVST to 3D data (in fact 2D-1D data) when the third dimension is not a spatial dimension, but the wavelength, the energy, or the time. We show that the MSVST can be used for detecting and characterizing astrophysical sources of high-energy gamma rays, using realistic simulated observations with the Large Area Telescope (LAT). The LAT was launched in June 2008 on the Fermi Gamma-ray Space Telescope mission. The MSVST algorithm is very fast relative to traditional likelihood model fitting, and permits efficient detection across the time dimension and immediate estimation of spectral properties. Astrophysical sources of gamma rays, especially active galaxies, are typically quite variable, and our current work may lead to a reliable method to quickly characterize the flaring properties of newly-detected sources.