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Author (up) Amerio, A.; Calore, F.; Serpico, P.D.; Zaldivar, B.
Title Deepening gamma-ray point-source catalogues with sub-threshold information Type Journal Article
Year 2024 Publication Journal of Cosmology and Astroparticle Physics Abbreviated Journal J. Cosmol. Astropart. Phys.
Volume 03 Issue 3 Pages 055 - 18pp
Keywords gamma ray theory; Frequentist statistics
Abstract We propose a novel statistical method to extend Fermi-LAT catalogues of highlatitude -y-ray sources below their nominal threshold. To do so, we rely on the determination of the differential source -count distribution of sub -threshold sources which only provides the statistical flux distribution of faint sources. 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, therefore complementing the source -count distribution with spatial information. Besides being useful to orient efforts towards multi -messenger and multi -wavelength identification of new -y-ray sources, we expect the results to be especially advantageous for statistical applications such as cross -correlation analyses.
Address [Amerio, Aurelio; Zaldivar, Bryan] Univ Valencia, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran 2, Paterna 46980, Spain, Email: aurelio.amerio@ific.uv.es;
Corporate Author Thesis
Publisher IOP Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1475-7516 ISBN Medium
Area Expedition Conference
Notes WOS:001194945600003 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 6032
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Author (up) Amerio, A.; Cuoco, A.; Fornengo, N.
Title Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning Type Journal Article
Year 2023 Publication Journal of Cosmology and Astroparticle Physics Abbreviated Journal J. Cosmol. Astropart. Phys.
Volume 09 Issue 9 Pages 029 - 39pp
Keywords gamma ray theory; Machine learning
Abstract We reconstruct the extra-galactic gamma-ray source-count distribution, or dN/dS, of resolved and unresolved sources by adopting machine learning techniques. Specifically, we train a convolutional neural network on synthetic 2-dimensional sky-maps, which are built by varying parameters of underlying source-counts models and incorporate the FermiLAT instrumental response functions. The trained neural network is then applied to the Fermi-LAT data, from which we estimate the source count distribution down to flux levels a factor of 50 below the Fermi-LAT threshold. We perform our analysis using 14 years of data collected in the (1, 10) GeV energy range. The results we obtain show a source count distribution which, in the resolved regime, is in excellent agreement with the one derived from cataloged sources, and then extends as dN/dS " S-2 in the unresolved regime, down to fluxes of 5 center dot 10-12 cm-2 s-1. The neural network architecture and the devised methodology have the flexibility to enable future analyses to study the energy dependence of the source-count distribution.
Address [Amerio, A.] Univ Valencia, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran 2, Paterna 46980, Spain, Email: aurelio.amerio@ific.uv.es;
Corporate Author Thesis
Publisher IOP Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1475-7516 ISBN Medium
Area Expedition Conference
Notes WOS:001097055700001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5785
Permanent link to this record