A robust neural determination of the source-count distribution of the Fermi-LAT sky at high latitudes

TL;DR

This paper employs neural ratio estimation to analyze 14 years of Fermi-LAT gamma-ray data, accurately identifying sources and reconstructing their distribution, thereby advancing understanding of high-latitude gamma-ray sky composition.

Contribution

It introduces a simulation-based inference approach using neural networks to detect gamma-ray sources and determine their distribution without relying on traditional catalog completeness.

Findings

Detected over 98% of sources in the 4FGL catalog above a certain flux threshold

Reconstructed source-count distribution consistent with previous studies

Validated the gamma-ray emission simulator with anomaly detection

Abstract

Over the past 16 years, the Fermi Large Area Telescope (LAT) has significantly advanced our view of the GeV gamma-ray sky, yet several key questions remain - such as the composition of the isotropic gamma-ray background, the origin of the Fermi Bubbles or the potential presence of signatures from exotic physics like dark matter. Addressing these challenges requires sophisticated astrophysical modeling and robust statistical methods capable of handling high-dimensional parameter spaces. In this work, we analyze 14 years of high-latitude ($|b|\geq30^{\circ}$) Fermi-LAT data in the range from 1 to 10 GeV using simulation-based inference (SBI) via neural ratio estimation. This approach allows us to detect individual gamma-ray sources and derive a list of significant gamma-ray emitters containing more than 98\% of all sources listed in the Fermi-LAT Fourth Source Catalog (4FGL) with a flux $S>3\times10^{-10}\;\mathrm{cm}^{-2}\,\mathrm{s}^{-1}$ (about a factor of three larger than the flux above which 4FGL is nearly complete), without any non-4FGL source detected in that flux range. Additionally, we reconstruct the source-count distribution in both parametric and non-parametric forms, achieving large agreement with previous literature results as well as those sources detected by our SBI pipeline. We also quantitatively validate our gamma-ray emission simulator via an anomaly detection technique, demonstrating that the synthetic data closely reproduces the complexity of the real observations.