PT Journal
AU Amerio, A
   Cuoco, A
   Fornengo, N
TI Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning
SO Journal of Cosmology and Astroparticle Physics
JI J. Cosmol. Astropart. Phys.
PY 2023
BP 029
EP 39pp
VL 09
IS 9
DI 10.1088/1475-7516/2023/09/029
LA English
DE gamma ray theory; 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.
ER