相关论文: Fast Bayesian Inference for Neutrino Non-Standard …

Variational mean field approximations tend to struggle with contemporary overparametrized deep neural networks. Where a Bayesian treatment is usually associated with high-quality predictions and uncertainties, the practical reality has been…

We study a Dark Matter (DM) model in which the dominant coupling to the standard model occurs through a neutrino-DM-scalar coupling. The new singlet scalar will generically have couplings to nuclei/electrons arising from renormalizable…

Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using a…

This study explores a two-component dark matter model in which one component, heavier dark matter, annihilates into a lighter dark matter. The lighter dark matter is expected to generate detectable signals in detectors due to its enhanced…

Nonstandard interaction is expected to be a crucial hint in explaining the experimental data on neutrino scattering off electrons. In this context, the nonstandard interaction vector and axial-vector couplings are needed to be extracted…

We demonstrate that current and planned underground neutrino experiments could offer a powerful probe of few-MeV dark matter when combined with a nearby high-intensity low-to-medium energy electron accelerator. This experimental setup, an…

We perform a first global exploration of the Constrained Next-to-Minimal Supersymmetric Standard Model using Bayesian statistics. We derive several global features of the model and find that, in some contrast to initial expectations, they…

This Report provides an extensive review of the experimental programme of direct detection searches of particle dark matter. It focuses mostly on European efforts, both current and planned, but does it within a broader context of a…

Dark Matter particles can be detected directly via their elastic scattering with nuclei. Next generation experiments can eventually find physical evidences about dark matter candidates. With this motivation in mind, we have calculated the…

We study the case of multi-component dark matter, in particular how direct detection signals are modified in the presence of several stable weakly-interacting-massive particles. Assuming a positive signal in a future direct detection…

The sensitivity of direct detection of dark matter (DM) approaches the so-called neutrino floor below which it is hard to disentangle the DM candidate from the background neutrino. In this work we consider the scenario that no DM signals…

A toy detector has been designed to simulate central detectors in reactor neutrino experiments in the paper. The samples of neutrino events and three major backgrounds from the Monte-Carlo simulation of the toy detector are generated in the…

Parameter inference is a fundamental problem in data-driven modeling. Given observed data that is believed to be a realization of some parameterized model, the aim is to find parameter values that are able to explain the observed data. In…

We present a comprehensive theoretical analysis of neutrino-induced decoherence in macroscopic matter-wave interferometry experiments designed to search for dark matter and beyond-Standard Model physics. Our calculation includes…

Searching for non-standard neutrino interactions, as a means for discovering physics beyond the Standard Model, has one of the key goals of dedicated neutrino experiments, current and future. We demonstrate here that much of the parameter…

Dark matter direct detection experiments have become excellent low-energy neutrino detectors. We present a few novel ideas to probe Beyond the Standard Model physics from neutrinos at these experiments. First, we discuss signatures arising…

We present a novel Bayesian inference tool that uses a neural network to parameterise efficient Markov Chain Monte-Carlo (MCMC) proposals. The target distribution is first transformed into a diagonal, unit variance Gaussian by a series of…

The neutrino physics program at the LHC, which will soon be initiated by the FASER experiment, will provide unique opportunities for precision studies of neutrino interaction vertices at high energies. This will also open up the possibility…

Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using…

Dark matter detection experiments are getting ever closer to the sensitivity needed to detect the primary particle physics candidates for nonbaryonic dark matter. Indirect detection methods include searching for antimatter and gamma rays,…