{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepening-gamma-ray-point-source-catalogues","title":"Deepening gamma-ray point-source catalogues with sub-threshold information","arxiv_id":"2306.16483","date":"2023-06-28","proceeding":null,"authors":["Aurelio Amerio","Francesca Calore","Pasquale Dario Serpico","Bryan Zaldivar"],"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.","url_abs":"https://arxiv.org/abs/2306.16483v2","url_pdf":"https://arxiv.org/pdf/2306.16483v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"deepening-gamma-ray-point-source-catalogues","repo_url":"https://github.com/aurelio-amerio/gpcs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}