activity
20232026
most citedExtracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning

6 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

GenSBI: Generative Methods for Simulation-Based Inference in JAX

Aurelio Amerio

Flow and diffusion generative models have established themselves as widely adopted density estimators for simulation-based inference (SBI), extending naturally from neural posterio…

astro-ph.HE2025★ 1 cited

Dark Matter and Galaxy Cross-Correlations with the Cherenkov Telescope Array Observatory

Elena Pinetti, Veronika Vodeb, Aurelio Amerio +4

The Cherenkov Telescope Array Observatory (CTAO) will be a ground-based Cherenkov telescope performing wide-sky surveys, ideal for anisotropy studies such as cross-correlations wit…

astro-ph.HE2025★ 1 cited

Search for dark matter subhalos among unassociated Fermi-LAT sources in presence of dataset shift

Aurelio Amerio, Dmitry Malyshev, Bryan Zaldivar +2

We present a search for dark matter (DM) annihilating subhalos of the Milky Way halo among the {\Fermi} Large Area Telescope (LAT) unassociated sources. For this purpose, we constr…

astro-ph.HE2024★ 1 cited

Millisecond Pulsars in Globular Clusters and Implications for the Galactic Center Gamma-Ray Excess

Aurelio Amerio, Dan Hooper, Tim Linden

We study the gamma-ray emission from millisecond pulsars within the Milky Way's globular cluster system in order to measure the luminosity function of this source population. We fi…

astro-ph.HE2023

Deepening gamma-ray point-source catalogues with sub-threshold information

Aurelio Amerio, Francesca Calore, Pasquale Dario Serpico +1

We propose a novel statistical method to extend Fermi-LAT catalogues of high-latitude -ray sources below their nominal threshold. To do so, we rely on a recent determination of…

astro-ph.CO2023★ 6 cited

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

Aurelio Amerio, Alessandro Cuoco, Nicolao Fornengo

We reconstruct the extra-galactic gamma-ray source-count distribution, or , of resolved and unresolved sources by adopting machine learning techniques. Specifically, we trai…