Abstract: Sterile neutrinos could provide a link between the Standard Model particles and a dark sector, besides generating active neutrino masses via the seesaw mechanism type I. We show that, if dark matter annihilation into sterile neutrinos determines its observed relic abundance, it is possible to explain the Galactic Center gamma-ray excess reported by the Fermi-LAT Collaboration as due to an astrophysical component plus dark matter annihilations. We observe that sterile neutrino portal to dark matter provides an impressively good fit, with a p-value of 0.78 in the best fit point, to the Galactic Center gamma-ray flux, for DM masses in the range (40-80) GeV and sterile neutrino masses 20 GeV less than or similar to M-N < M-DM. Such values are compatible with the limits from Fermi-LAT observations of the dwarfs spheroidal galaxies in the Milky Way halo, which rule out dark matter masses below similar to 50 GeV ( 90 GeV), for sterile neutrino masses M-N less than or similar to MDM ( M-N << M-DM). We also estimate the impact of AMS-02 anti-proton data on this scenario.

Abstract: Dark matter annihilations in the Galactic halo inject relativistic electrons and positrons which in turn generate a synchrotron radiation when interacting with the galactic magnetic field. We calculate the synchrotron flux for various dark matter annihilation channels, masses, and astrophysical assumptions in the low-frequency range and compare our results with radio surveys from 22 MHz to 1420 MHz. We find that current observations are able to constrain particle dark matter with “thermal” annihilation cross-sections, i.e. (sigma v) = 3 x 10(-26) cm(3) s(-1); and masses M-DM less than or similar to 10 GeV. We discuss the dependence of these bounds on the astrophysical assumptions, namely galactic dark matter distribution, cosmic rays propagation parameters, and structure of the galactic magnetic field. Prospects for detection in future radio surveys are outlined.

Abstract: Around one-third of the point-like sources in the Fermi-LAT catalogues remain as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association with a known astrophysical source. If dark matter (DM) is composed of weakly interacting massive particles (WIMPs), there is the exciting possibility that some of these unIDs may actually be DM sources, emitting gamma-rays from WIMPs annihilation. We propose a new approach to solve the standard, machine learning (ML) binary classification problem of disentangling prospective DM sources (simulated data) from astrophysical sources (observed data) among the unIDs of the 4FGL Fermi-LAT catalogue. We artificially build two systematic features for the DM data which are originally inherent to observed data: the detection significance and the uncertainty on the spectral curvature. We do it by sampling from the observed population of unIDs, assuming that the DM distributions would, if any, follow the latter. We consider different ML models: Logistic Regression, Neural Network (NN), Naive Bayes, and Gaussian Process, out of which the best, in terms of classification accuracy, is the NN, achieving around 93 . 3 per cent +/- 0 . 7 per cent performance. Other ML evaluation parameters, such as the True Ne gativ e and True Positive rates, are discussed in our work. Applying the NN to the unIDs sample, we find that the de generac y between some astrophysical and DM sources can be partially solved within this methodology. None the less, we conclude that there are no DM source candidates among the pool of 4FGL Fermi-LAT unIDs.