D3PO - Denoising, Deconvolving, and Decomposing Photon Observations

The analysis of astronomical images is a non-trivial task. The DPO algorithm addresses the inference problem of denoising, deconvolving, and decomposing photon observations. The primary goal is the simultaneous reconstruction of the diffuse and point-like photon flux from a given photon count image. In order to discriminate between these morphologically different signal components, a probabilistic algorithm is derived in the language of information field theory based on a hierarchical Bayesian parameter model. The signal inference exploits prior information on the spatial correlation structure of the diffuse component and the brightness distribution of the spatially uncorrelated point-like sources. A maximum a posteriori solution and a solution minimizing the Gibbs free energy of the inference problem using variational Bayesian methods are discussed. Since the derivation of the solution does not dependent on the underlying position space, the implementation of the DPO algorithm uses the NIFTy package to ensure operationality on various spatial grids and at any resolution. The fidelity of the algorithm is validated by the analysis of simulated data, including a realistic high energy photon count image showing a 32× 32 arcmin observation with a spatial resolution of 0.1 arcmin. In all tests the DPO algorithm successfully denoised, deconvolved, and decomposed the data into a diffuse and a point-like signal estimate for the respective photon flux components.