Abstract: We reconsider the discovery limit of multi-ton direct detection dark matter experiments in the light of recent measurements of the coherent elastic neutrino-nucleus scattering process. Assuming the cross section to be a parameter entirely determined by data, rather than using its Standard Model prediction, we use the COHERENT CsI and LAr data sets to determine WIMP discovery limits. Being based on a data-driven approach, the results are thus free from theoretical assumptions and fall within the WIMP mass regions where XENONnT and DARWIN have best expected sensitivities. We further determine the impact of subleading nuclear form factor and weak mixing angle uncertainties effects on WIMP discovery limits. We point out that these effects, albeit small, should be taken into account. Moreover, to quantify the impact of new physics effects in the neutrino background, we revisit WIMP discovery limits assuming light vector and scalar mediators as well as neutrino magnetic moments/transitions. We stress that the presence of new interactions in the neutrino sector, in general, tend to worsen the WIMP discovery limit.

Abstract: Cosmological simulations play a pivotal role in understanding the properties of the dark matter (DM) distribution in both galactic and galaxy -cluster environments. The characterization of DM structures is crucial for informing indirect DM searches, aiming at the detection of the annihilation (or decay) products of DM particles. A fundamental quantity in these analyses is the astrophysical J -factor. In the DM phenomenology community, J -factors are typically computed through the semi -analytical modelling of the DM mass distribution, which is affected by large uncertainties. With the scope of addressing and possibly reducing these uncertainties, we present NAJADS, a self-contained framework to derive the DM J -factor directly from the raw simulations data. We show how this framework can be used to compute all -sky maps of the J -factor, automatically accounting for the complex 3D structure of the simulated halos and for the boosting of the signal due to the density fluctuations along the line of sight. After validating our code, we present a proof -of -concept application of NAJADS to a realistic halo from the IllustrisTNG suite, and exploit it to make a thorough comparison between our numerical approach and traditional semi -analytical methods. JCAP02(2024)001

Abstract: One of the most promising strategies to identify the nature of dark matter consists in the search for new particles at accelerators and with so-called direct detection experiments. Working within the framework of simplified models, and making use of machine learning tools to speed up statistical inference, we address the question of what we can learn about dark matter from a detection at the LHC and a forthcoming direct detection experiment. We show that with a combination of accelerator and direct detection data, it is possible to identify newly discovered particles as dark matter, by reconstructing their relic density assuming they are weakly interacting massive particles (WIMPs) thermally produced in the early Universe, and demonstrating that it is consistent with the measured dark matter abundance. An inconsistency between these two quantities would instead point either towards additional physics in the dark sector, or towards a non-standard cosmology, with a thermal history substantially different from that of the standard cosmological model.