arXiv subjects

Aurelio Amerio

Publications and source records attributed to Aurelio Amerio.

6 recordsLinked to original sources

arXiv2026

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

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 construct the first statistical model of the unassociated sources at latitudes above 10 degrees, combining potential DM subhalos with Galactic and extragalactic astrophysical components. The distributions of astrophysical sources are constructed based on associated sources, while the DM subhalo distribution is derived from Monte Carlo simulations. We account for differences between associated and unassociated source distributions using a model with covariate and prior probability shifts, which are particular cases of more general dataset shifts. This approach is based on quantification learning, advancing beyond previous classify-and-count strategies by providing a well-defined statistical interpretation of the potential contribution from a DM subhalo population. For the $b\bar{b}$ annihilation channel and DM masses from 10 GeV to 1 TeV, we find no significant contribution from DM subhalos, and therefore derive 95% confidence upper limits on the annihilation cross section. Our analysis yields limits consistent with previous classify-and-count approaches, while the underlying generative model provides a more robust statistical framework, opening new avenues for population studies of Fermi-LAT sources and, more generally, for searches of anomalies, such as a new class of sources in addition to the known classes of sources, in presence of statistical and systematic uncertainties.

astro-ph.HE↗

arXiv2026

GenSBI: Generative Methods for Simulation-Based Inference in JAX

Flow and diffusion generative models have established themselves as widely adopted density estimators for simulation-based inference (SBI), extending naturally from neural posterior estimation to likelihood and joint density estimation. Their principled optimization objectives and freedom from architectural constraints have driven rapid adoption across the natural sciences. Yet the most widely used SBI libraries remain PyTorch-based, leaving researchers who develop their forward models and analysis pipelines in JAX without a native option. We present GenSBI, an open-source library that implements flow matching, score matching, and denoising diffusion entirely in JAX. The library offers three transformer-based architectures - SimFormer, Flux1, and a novel Flux1Joint that extends gate-modulated transformer blocks to joint density estimation - all interchangeable through a unified interface that decouples generative method, neural backbone, and inference mode. GenSBI provides an end-to-end workflow from training through posterior calibration (SBC, TARP, LC2ST) and supports custom architectures with domain-specific embedding networks. We validate the framework on standard SBI benchmarks, achieving near-ideal mean C2ST scores (0.50-0.56, where 0.50 is ideal) on SBIBM tasks with minimal per-task tuning and well-calibrated posterior coverage across all tested configurations. The code is publicly available at https://github.com/aurelio-amerio/GenSBI.

cs.LG↗

arXiv2026

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

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 find that these pulsars have a mean luminosity of $\langle L_γ\rangle \sim (1-8)\times 10^{33}\, {\rm erg/s}$ (integrated between 0.1 and 100 GeV) and a log-normal width of $σ_L \sim 1.4-2.8$. If the Galactic Center Gamma-Ray Excess were produced by pulsars with similar characteristics, Fermi would have already detected $N \sim 17-37$ of these sources, whereas only three such pulsar candidates have been identified. We conclude that the excess gamma-ray emission can originate from pulsars only if they are significantly less bright, on average, than those observed within globular clusters or in the Galactic Plane. This poses a serious challenge for pulsar interpretations of the Galactic Center Gamma-Ray Excess.

astro-ph.HE↗

arXiv2026

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

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 with tracers of the cosmic large-scale structure. Cross-correlations can shed light on high-energy $γ$-ray sources and potentially reveal exotic signals from particle dark matter. In this work, we investigate CTAO sensitivity to cross-correlation signals between $γ$-ray emission and galaxy distributions. We find that by using dense, low-redshift catalogs like 2MASS, and for integration times around 50 hours, this technique achieves sensitivities to both annihilating and decaying dark matter signals that are competitive with those from dwarf galaxy and cluster analyses.

astro-ph.HE↗

arXiv2024

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

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.

astro-ph.CO↗

arXiv2024

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

We propose a novel statistical method to extend Fermi-LAT catalogues of high-latitude $γ$-ray sources below their nominal threshold. To do so, we rely on a recent determination of the differential source-count distribution of sub-threshold sources via the application of deep learning methods to the $γ$-ray sky. By simulating ensembles of synthetic skies, we assess quantitatively the likelihood for pixels in the sky with relatively low-test statistics to be due to sources. Besides being useful to orient efforts towards multi-messenger and multi-wavelength identification of new $γ$-ray sources, we expect the results to be especially advantageous for statistical applications such as cross-correlation analyses.

astro-ph.HE↗