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

The multiscale variance stabilization Transform (MSVST) has recently been proposed for Poisson data denoising (Zhang et al. 2008a). This procedure, which is nonparametric, is based on thresholding wavelet coefficients. The restoration algorithm applied after thresholding provides good conservation of source flux. 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. Source detection in the LAT data is complicated by the low fluxes of point sources relative to the diffuse celestial foreground, the limited angular resolution, and the tremendous variation in that resolution with energy (from tens of degrees at similar to 30 MeV to similar to 0.1 degrees at 10 GeV). The high-energy gamma-ray sky is also quite dynamic, with a large population of sources such active galaxies with accretion-powered black holes producing high-energy jets, episodically flaring. The fluxes of these sources can change by an order of magnitude or more on time scales of hours. Perhaps the majority of blazars will have average fluxes that are too low to be detected but could be found during the hours or days that they are flaring. 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.