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

TL;DR

This paper introduces a new statistical approach that leverages deep learning to extend gamma-ray source catalogs by identifying sources below the detection threshold, enhancing the understanding of the gamma-ray sky.

Contribution

The paper presents a novel method combining deep learning and sky simulations to statistically identify sub-threshold gamma-ray sources, improving catalog completeness.

Findings

Quantitative likelihood assessment for low-test statistic pixels

Enhanced identification of sub-threshold gamma-ray sources

Potential for improved multi-messenger and multi-wavelength studies

Abstract

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