Source Detection Using a 3D Sparse Representation: Application to the Fermi Gamma-ray Space Telescope

Jean-Luc Starck (1) , Jalal M. Fadili (2) , Seth W. Digel (3) , Bo Zhang (4) , Jim Chiang (3)

1 IRFU - Institut de Recherches sur les lois Fondamentales de l'Univers
2 Equipe Image - Laboratoire GREYC - UMR6072
3 SLAC - SLAC National Accelerator Laboratory
4 AIQ - Analyse d'Images Quantitative

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.

Mots clés: methods: data analysis; techniques: image processing

Domaines: Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Jean-Luc Starck, Jalal M. Fadili, Seth W. Digel, Bo Zhang, Jim Chiang. Source Detection Using a 3D Sparse Representation: Application to the Fermi Gamma-ray Space Telescope. Astronomy and Astrophysics - A&A, 2009, 504 (2), pp.641-652. ⟨10.1051/0004-6361/200811388⟩. ⟨hal-00813995⟩