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- W4386738295 abstract "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 Fermi -LAT 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 · 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." @default.
- W4386738295 created "2023-09-15" @default.
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- W4386738295 date "2023-09-01" @default.
- W4386738295 modified "2023-09-26" @default.
- W4386738295 title "Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning" @default.
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- W4386738295 doi "https://doi.org/10.1088/1475-7516/2023/09/029" @default.
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