Abstract: Sterile neutrinos with a mass in the eV range have been invoked as a possible explanation of a variety of short baseline (SBL) neutrino oscillation anomalies. However, if one considers neutrino oscillations between active and sterile neutrinos, such neutrinos would have been fully thermalised in the early universe, and would be therefore in strong conflict with cosmological bounds. In this study we first update cosmological bounds on the mass and energy density of eV-scale sterile neutrinos. We then perform an updated study of a previously proposed model in which the sterile neutrino couples to a new light pseudoscalar degree of freedom. Consistently with previous analyses, we find that the model provides a good fit to all cosmological data and allows the high value of H-0 measured in the local universe to be consistent with measurements of the cosmic microwave background. However, new high l polarisation data constrain the sterile neutrino mass to be less than approximately 1 eV in this scenario. Finally, we combine the cosmological bounds on the pseudoscalar model with a Bayesian inference analysis of SBL data and conclude that only a sterile mass in narrow ranges around 1 eV remains consistent with both cosmology and SBL data.

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: Coherent Elastic neutrino-Nucleus Scattering (CE nu NS), a process recently measured for the first time at ORNL's Spallation Neutron Source, is directly sensitive to the weak form factor of the nucleus. The European Spallation Source (ESS), presently under construction, will generate the most intense pulsed neutrino flux suitable for the detection of CE nu NS. In this paper we quantify its potential to determine the root mean square radius of the point-neutron distribution, for a variety of target nuclei and a suite of detectors. To put our results in context we also derive, for the first time, a constraint on this parameter from the analysis of the energy and timing data of the CsI detector at the COHERENT experiment.

Abstract: Understanding the halo-galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase space, we use Graph Neural Networks (GNNs), which are designed to work with irregular and sparse data. We train our models on galaxies from more than 2000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations project. Our model, which accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a similar to 0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method. The PyTorch Geometric implementation of the GNN is publicly available on GitHub (https://github.com/PabloVD/HaloGraphNet).

Abstract: We present an updated and improved global fit analysis of current flavour and electroweak precision observables to derive bounds on unitarity deviations of the leptonic mixing matrix and on the mixing of heavy neutrinos with the active flavours. This new analysis is motivated by new and updated experimental results on key observables such as V-ud, the invisible decay width of the Z boson and the W boson mass. It also improves upon previous studies by considering the full correlations among the different observables and explicitly calibrating the test statistic, which may present significant deviations from a & chi;(2) distribution. The results are provided for three different Type-I seesaw scenarios: the minimal scenario with only two additional right-handed neutrinos, the next to minimal one with three extra neutrinos, and the most general one with an arbitrary number of heavy neutrinos that we parametrise via a generic deviation from a unitary leptonic mixing matrix. Additionally, we also analyze the case of generic deviations from unitarity of the leptonic mixing matrix, not necessarily induced by the presence of additional neutrinos. This last case relaxes some correlations among the parameters and is able to provide a better fit to the data. Nevertheless, inducing only leptonic unitarity deviations avoiding both the correlations implied by the right-handed neutrino extension as well as more strongly constrained operators is challenging and would imply significantly more complex UV completions.