Ensemble-based methods are among the state-of-the-art assimilation algorithms in the data assimilation community. 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.ĭata assimilation is an important discipline in geosciences that aims to combine the information contents from both prior geophysical models and observational data (observations) to obtain improved model estimates. 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. The LAT was launched in June 2008 on the Fermi Gamma-ray Space Telescope mission. 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). 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. This procedure, which is nonparametric, is based on thresholding wavelet coefficients. The multiscale variance stabilization Transform (MSVST) has recently been proposed for Poisson data denoising.
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