Deepening gamma-ray point-source catalogues with sub-threshold information
arxiv(2023)
摘要
We propose a novel statistical method to extend Fermi-LAT catalogues of
high-latitude γ-ray sources below their nominal threshold. To do so, we
rely on a recent determination of the differential source-count distribution of
sub-threshold sources via the application of deep learning methods to the
γ-ray sky. By simulating ensembles of synthetic skies, we assess
quantitatively the likelihood for pixels in the sky with relatively low-test
statistics to be due to sources. Besides being useful to orient efforts towards
multi-messenger and multi-wavelength identification of new γ-ray
sources, we expect the results to be especially advantageous for statistical
applications such as cross-correlation analyses.
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