Related papers: Fast Bayesian Inference for Neutrino Non-Standard …
We present a detailed analysis on nonstandard neutrino interactions (NSI) with electrons including all muon and electron (anti)-neutrino data from existing accelerators and reactors, in conjunction with the ``neutrino counting'' data (e- e+…
A wide range of techniques have been developed to search for particle dark matter, including direct detection, indirect detection, and collider searches. The prospects for the detection of neutralino dark matter is quite promising for each…
Ensembles of deep neural networks demonstrate improved performance over single models. For enhancing the diversity of ensemble members while keeping their performance, particle-based inference methods offer a promising approach from a…
In this paper, we present a novel approach to accelerate the Bayesian inference process, focusing specifically on the nested sampling algorithms. Bayesian inference plays a crucial role in cosmological parameter estimation, providing a…
In addition to their broad physics reach enabled by their high energies and precision, future multi-TeV muon colliders will also be the world's most intense sources of neutrinos. This offers the opportunity to search for new non-standard…
The sensitivity to dark matter signals at neutrino experiments is fundamentally challenged by the neutrino rates, as they leave similar signatures in their detectors. As a way to improve the signal sensitivity, we investigate a dark matter…
Dark matter and neutrinos provide the two most compelling pieces of evidence for new physics beyond the Standard Model of Particle Physics but they are often treated as two different sectors. The aim of this paper is to determine whether…
We study the sensitivity of current and future long-baseline neutrino oscillation experiments to the effects of dimension six operators affecting neutrino propagation through Earth, commonly referred to as Non-Standard Interactions (NSI).…
We compare the first results on searches for supersymmetry with the Large Hadron Collider (LHC) to the current and near-term performance of experiments sensitive to neutralino dark matter. We limit our study to the particular slices of…
In the absence of direct accelerator data to constrain particle models, and given existing astrophysical uncertainties associated with the phase space distribution of WIMP dark matter in our galactic halo, extracting information on…
Neutrino experiments study the least understood of the Standard Model particles by observing their direct interactions with matter or searching for ultra-rare signals. The study of neutrinos typically requires overcoming large backgrounds,…
Almost all fields of science rely upon statistical inference to estimate unknown parameters in theoretical and computational models. While the performance of modern computer hardware continues to grow, the computational requirements for the…
Neutrino non-standard interactions (NSI) with the first generation of standard model fermions can span a parameter space of large dimension and exhibit degeneracies that cannot be broken by a single class of experiment. Oscillation…
Inference of latent feature models in the Bayesian nonparametric setting is generally difficult, especially in high dimensional settings, because it usually requires proposing features from some prior distribution. In special cases, where…
The direct detection of neutralino dark matter is analysed in general supergravity scenarios, where non-universal soft scalar and gaugino masses can be present. In particular, the theoretical predictions for the neutralino-nucleon cross…
The signals from outer space and their detection have been playing an important role in particle physics, especially in discoveries of and searches for physics beyond the Standard Model (BSM); beyond the evidence of dark matter (DM), for…
In this paper we assess the present status of dark matter direct searches by means of Bayesian statistics. We consider three particle physics models for spin-independent dark matter interaction with nuclei: elastic, inelastic and isospin…
Nested sampling is a promising tool for Bayesian statistical analysis because it simultaneously performs parameter estimation and facilitates model comparison. MultiNest is one of the most popular nested sampling implementations, and has…
Direct detection dark matter experiments looking for WIMP-nucleus elastic scattering will soon be sensitive to an irreducible background from neutrinos which will drastically affect their discovery potential. Here we explore how the…
We carry out a Bayesian analysis of dark matter (DM) direct detection data to determine particle model parameters using the Truncated Marginal Neural Ratio Estimation (TMNRE) machine learning technique. TMNRE avoids an explicit calculation…