Deepening gamma-ray point-source catalogues with sub-threshold information

Abstract

We propose a novel statistical method to extend Fermi-LAT catalogues of high-latitude ${\hat{I}}^3$-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 ${\hat{I}}^3$-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 ${\hat{I}}^3$-ray sources, we expect the results to be especially advantageous for statistical applications such as cross-correlation analyses.

12 pages, 3 figures. For the associated Python code, see https://doi.org/10.5281/zenodo.8070852