Abstract: This work presents a study of the Al-27(alpha, n) reaction over an extended energy range (3.5-15 MeV) relative to previous datasets, performed at the Centro de Micro-An & aacute;lisis de Materiales in Madrid. Thick-target yields were obtained from activation by the online measurement of the decay of the P-30 reaction product, using the recently developed Gamma-detection Array for Reaction Yield measurements. The results show good agreement with existing data and confirm the newest thick-target yields at low energies. Direct Al-27(alpha, n gamma)P-30 gamma-yields were measured in order to address (alpha, n) branches to excited states in P-30. The angular dependence of the emitted prompt gamma rays was investigated, revealing significant differences as a function of angle with respect to the beam direction. The results provide benchmark data for future (alpha, n) yield studies using online activation measurements and prompt gamma-ray detection.

Abstract: 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 FermiLAT 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 center dot 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.

Abstract: 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 bb & strns; 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.