1. Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning
- Author
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Amerio, Aurelio, Cuoco, Alessandro, and Fornengo, Nicolao
- Subjects
High Energy Astrophysical Phenomena (astro-ph.HE) ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,Cosmology and Nongalactic Astrophysics (astro-ph.CO) ,FOS: Physical sciences ,Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Instrumentation and Methods for Astrophysics ,Instrumentation and Methods for Astrophysics (astro-ph.IM) ,Astrophysics - Cosmology and Nongalactic Astrophysics ,Machine Learning (cs.LG) - 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 catalogued sources, and then extends as $dN/dS \sim S^{-2}$ in the unresolved regime, down to fluxes of $5 \cdot 10^{-12}$ 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., Comment: 25 pages + Appendix, 24 figures
- Published
- 2023
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