@Article{Amerio_etal2023,
author="Amerio, A.
and Cuoco, A.
and Fornengo, N.",
title="Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning",
journal="Journal of Cosmology and Astroparticle Physics",
year="2023",
publisher="IOP Publishing Ltd",
volume="09",
number="9",
pages="029--39pp",
optkeywords="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 {\textquoteleft}{\textquoteleft} 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.",
optnote="WOS:001097055700001",
optnote="exported from refbase (https://references.ific.uv.es/refbase/show.php?record=5785), last updated on Sat, 25 Nov 2023 13:36:55 +0000",
issn="1475-7516",
doi="10.1088/1475-7516/2023/09/029",
opturl="https://arxiv.org/abs/2302.01947",
opturl="https://doi.org/10.1088/1475-7516/2023/09/029",
language="English"
}