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

Christopher Eckner1,2,*, Noemi Anau Montel3,4,†, Florian List4,5,‡, Francesca Calore2,§, and Christoph Weniger3,∥

  • 1Center for Astrophysics and Cosmology, University of Nova Gorica, Vipavska 11c, 5270 Ajdovščina, Slovenia
  • 2LAPTh, CNRS, F-74000 Annecy, France
  • 3GRAPPA (Gravitation Astroparticle Physics Amsterdam), University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
  • 4Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str. 1, 85748 Garching, Germany
  • 5Department of Astrophysics, University of Vienna, Türkenschanzstraße 17, 1180 Vienna, Austria

  • *Contact author: ceckner@ung.si
  • †Contact author: noemiam@mpa-garching.mpg.de
  • ‡Contact author: florian.list@univie.ac.at
  • §Contact author: calore@lapth.cnrs.fr
  • ∥Contact author: c.weniger@uva.nl
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Phys. Rev. D 112, 103022 – Published 12 November, 2025

DOI: https://doi.org/10.1103/p4gf-d2zt

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|≥30°) 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>3×10−10  cm−2 s−1 (about a factor of 3 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 nonparametric 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 reproduce the complexity of the real observations.

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Phys. Rev. D 112, 103022– Published 12 November, 2025
  • Received 19 May 2025
  • Accepted 14 October 2025
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