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Author  |
Amerio, A.; Cuoco, A.; Fornengo, N. |
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Title |
Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning |
Type |
Journal Article |
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Year |
2023 |
Publication |
Journal of Cosmology and Astroparticle Physics |
Abbreviated Journal |
J. Cosmol. Astropart. Phys. |
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Volume |
09 |
Issue |
9 |
Pages |
029 - 39pp |
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Keywords |
gamma ray theory; Machine learning |
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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. |
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Address |
[Amerio, A.] Univ Valencia, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran 2, Paterna 46980, Spain, Email: aurelio.amerio@ific.uv.es; |
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Publisher |
IOP Publishing Ltd |
Place of Publication |
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Language |
English |
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Original Title |
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Series Volume |
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Edition |
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ISSN |
1475-7516 |
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Notes |
WOS:001097055700001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5785 |
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