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Abstract Multi-dimensional parameter spaces are commonly encountered in physics theories that go beyond the Standard Model. However, they often possess complicated posterior geometries that are expensive to traverse using techniques traditional to astroparticle physics. Several recent innovations, which are only beginning to make their way into this field, have made navigating such complex posteriors possible. These include GPU acceleration, automatic differentiation, and neural-network-guided reparameterization. We apply these advancements to dark matter direct detection experiments in the context of non-standard neutrino interactions and benchmark their performances against traditional nested sampling techniques when conducting Bayesian inference. Compared to nested sampling alone, we find that these techniques increase performance for both nested sampling and Hamiltonian Monte Carlo, accelerating inference by factors of $$\sim 100$$ and $$\sim 60$$, respectively. As nested sampling also evaluates the Bayesian evidence, these advancements can be exploited to improve model comparison performance while retaining compatibility with existing implementations that are widely used in the natural sciences. Using these techniques, we perform the first scan in the neutrino non-standard interactions parameter space for direct detection experiments whereby all parameters are allowed to vary simultaneously. We expect that these advancements are broadly applicable to other areas of astroparticle physics featuring multi-dimensional parameter spaces. 

Abstract (Ultra)light spin-1 particles — dark photons — can constitute all of dark matter (DM) and have beyond Standard Model couplings. This can lead to a coherent, oscillatory signature in terrestrial detectors that depends on the coupling strength. We provide a signal analysis and statistical framework for inferring the properties of such DM by taking into account (i) the stochastic and (ii) the vector nature of the underlying field, along with (iii) the effects due to the Earth's rotation. Owing to equipartition, on time scales shorter than the coherence time the DM field vector typically traces out a fixed ellipse. Taking this ellipse and the rotation of the Earth into account, we highlight a distinctive three-peak signal in Fourier space that can be used to constrain DM coupling strengths. Accounting for all three peaks, we derive latitude-independent constraints on such DM couplings, unlike those stemming from single-peak studies. We apply our framework to the search for ultralightB - LDM using optomechanical sensors, demonstrating the ability to delve into previously unprobed regions of this DM candidate's parameter space. 

A<sc>bstract</sc> In this article, we study the potential of direct detection experiments to explore the parameter space of general non-standard neutrino interactions (NSI) via solar neutrino scattering. Due to their sensitivity to neutrino-electron and neutrino-nucleus scattering, direct detection provides a complementary view of the NSI landscape to that of spallation sources and neutrino oscillation experiments. In particular, the large admixture of tau neutrinos in the solar flux makes direct detection experiments well-suited to probe the full flavour space of NSI. To study this, we develop a re-parametrisation of the NSI framework that explicitly includes a variable electron contribution and allows for a clear visualisation of the complementarity of the different experimental sources. Using this new parametrisation, we explore how previous bounds from spallation source and neutrino oscillation experiments are impacted. For the first time, we compute limits on NSI from the first results of the XENONnT and LUX-ZEPLIN experiments, and we obtain projections for future xenon-based experiments. These computations have been performed with our newly developed software package, SNuDD. Our results demonstrate the importance of using a more general NSI parametrisation and indicate that next generation direct detection experiments will become powerful probes of neutrino NSI.