Characterizing the expected behavior of non-Poissonian template fitting

Bariuan, Luis Gabriel C.; Slatyer, Tracy R.

doi:10.1103/physrevd.107.103014

Cited by 1 publication

(1 citation statement)

References 59 publications

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“…This technique has then been used to study the contribution of individual source classes of emitters, in particular blazars [12][13][14][15]. The same methodology has also been used to characterize the properties of the unresolved sources in the Galactic Center region in relation to a possible signal from dark matter annihilation [16][17][18][19][20][21][22][23][24][25]. A further methodology called Compound Poisson Generator has also been developed [26], in order to handle biases possibly present in the 1-point PDF method.…”

Section: Jcap09(2023)029mentioning

confidence: 99%

Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning

Amerio,

Cuoco,

Fornengo

2023

J. Cosmol. Astropart. Phys.

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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.

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