TY  - JOUR
AU  - Amerio, A.
AU  - Cuoco, A.
AU  - Fornengo, N.
PY  - 2023
DA  - 2023//
TI  - Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning
T2  - J. Cosmol. Astropart. Phys.
JO  - Journal of Cosmology and Astroparticle Physics
SP  - 029
EP  - 39pp
VL  - 09
IS  - 9
PB  - IOP Publishing Ltd
KW  - gamma ray theory
KW  - Machine learning
AB  - 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.
SN  - 1475-7516
UR  - https://arxiv.org/abs/2302.01947
UR  - https://doi.org/10.1088/1475-7516/2023/09/029
DO  - 10.1088/1475-7516/2023/09/029
LA  - English
N1  - WOS:001097055700001
ID  - Amerio_etal2023
ER  -