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Theme: Other creative topics in astronomical software
We present a new software for Joint Likelihood Deconvolution (Jolideco) of a set of astronomical
observations of the same sky region in the presence of Poisson noise. The method reconstructs a
single flux image from a set of observations by optimizing the a posteriori joint Poisson likelihood of all
observations under a patch based image prior. The patch prior is parameterised by a Gaussian Mixture
model (GMM) which we trained on astronomical images with high signal to noise ratio, including data
from the James Webb Telescope as well as the GLEAM radio survey. During the reconstruction
process the patch prior adapts to the patch structures in the data by finding the most likely GMM
component for each patch in the image. By applying the method to simulated data we show that both
the combination of multiple observations as well as the patch based prior lead to a much improved
reconstruction quality in many different source scenarios as well as signal to noise regimes. We show
also that the method yields superior reconstruction quality to alternative standard methods such as
the Richardson-Lucy method. We also the results of the method applied to example data from the
Chandra observatory as well as the Fermi -LAT.