Abstract: Since 2015 the LIGO and Virgo interferometers have detected gravitational waves from almost one hundred coalescences of compact objects (black holes and neutron stars). This article presents the results of a search performed with data from the ANTARES telescope to identify neutrino counterparts to the gravitational wave sources detected during the third LIGO/Virgo observing run and reported in the catalogues GWTC-2, GWTC-2.1, and GWTC-3. This search is sensitive to all-sky neutrinos of all flavours and of energies > 100 GeV, thanks to the inclusion of both track-like events (mainly induced by v μcharged -current interactions) and shower-like events (induced by other interaction types). Neutrinos are selected if they are detected within +/- 500 s from the GW merger and with a reconstructed direction compatible with its sky localisation. No significant excess is found for any of the 80 analysed GW events, and upper limits on the neutrino emission are derived. Using the information from the GW catalogues and assuming isotropic emission, upper limits on the total energy Etot,v emitted as neutrinos of all flavours and on the ratio fv = Etot,v/EGW between neutrino and GW emissions are also computed. Finally, a stacked analysis of all the 72 binary black hole mergers (respectively the 7 neutron star-black hole merger candidates) has been performed to constrain the typical neutrino emission within this population, leading to the limits: Etot,v < 4.0 x 1053 erg and fv < 0.15 (respectively, Etot,v < 3.2 x 1053 erg and fv < 0.88) for E-2 spectrum and isotropic emission. Other assumptions including softer spectra and non-isotropic scenarios have also been tested.

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: The Galactic Center Excess (GCE) in GeV gamma rays has been debated for over a decade, with the possibility that it might be due to dark matter annihilation or undetected point sources such as millisecond pulsars (MSPs). This study investigates how the gamma-ray emission model (-yEM) used in Galactic center analyses affects the interpretation of the GCE's nature. To address this issue, we construct an ultra-fast and powerful inference pipeline based on convolutional Deep Ensemble Networks. We explore the two main competing hypotheses for the GCE using a set of-yEMs with increasing parametric freedom. We calculate the fractional contribution (fsrc) of a dim population of MSPs to the total luminosity of the GCE and analyze its dependence on the complexity of the ryEM. For the simplest ryEM, we obtain fsrc = 0.10 f 0.07, while the most complex model yields fsrc = 0.79 f 0.24. In conclusion, we find that the statement about the nature of the GCE (dark matter or not) strongly depends on the assumed ryEM. The quoted results for fsrc do not account for the additional uncertainty arising from the fact that the observed gamma-ray sky is out-of-distribution concerning the investigated ryEM iterations. We quantify the reality gap between our ryEMs using deep-learning-based One-Class Deep Support Vector Data Description networks, revealing that all employed ryEMs have gaps to reality. Our study casts doubt on the validity of previous conclusions regarding the GCE and dark matter, and underscores the urgent need to account for the reality gap and consider previously overlooked “out of domain” uncertainties in future interpretations.