相关论文: Fast Bayesian Inference for Neutrino Non-Standard …
A fraction of the dark matter may consist of a particle species that interacts much more strongly with the Standard Model than a typical weakly interacting massive particle (WIMP) of similar mass. Such a strongly interacting dark matter…
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approaches asymptotically recover the true posterior but are…
We study the question of whether coherent neutrino scattering can occur on macroscopic scales, leading to a significant increase of the detection cross section. We concentrate on radiative neutrino scattering on atomic electrons (or on free…
We investigate the feasibility of the indirect detection of dark matter in a simple model using the neutrino portal. The model is very economical, with right-handed neutrinos generating neutrino masses through the Type-I seesaw mechanism…
Most applications of Bayesian Inference for parameter estimation and model selection in astrophysics involve the use of Monte Carlo techniques such as Markov Chain Monte Carlo (MCMC) and nested sampling. However, these techniques are time…
Parameterized partial differential equations (PDEs) underpin the mathematical modeling of complex systems in diverse domains, including engineering, healthcare, and physics. A central challenge in using PDEs for real-world applications is…
In some scenarios, the dark matter particle predominantly scatters inelastically with the target, producing a heavier neutral particle in the final state. In this class of scenarios, the reach in parameter space of direct detection…
The effective theory of isoscalar dark matter-nucleon interactions mediated by heavy spin-one or spin-zero particles depends on 10 coupling constants besides the dark matter particle mass. Here we compare this 11-dimensional effective…
Dark matter (DM) continues to evade direct detection, but neutron stars (NSs) serve as natural laboratories where even a modest DM component can alter their structure. While many studies have examined DM effects on NSs, they often rely on…
We address the direct detection of neutralino dark matter in the framework of the Next-to-Minimal Supersymmetric Standard Model. We conduct a detailed analysis of the parameter space, taking into account all the available constraints from…
Compared to other neutrino sources, the huge anti-neutrino fluxes at nuclear reactor based experiments empower us to derive stronger bounds on non-standard interactions of neutrinos with electrons mediated by light scalar/vector mediators.…
We simultaneously investigate source, detector and matter non-standard neutrino interactions at the proposed DUNE experiment. Our analysis is performed using a Markov Chain Monte Carlo exploring the full parameter space. We find that the…
Neutrino experiments are often limited by low statistics, sizable systematic uncertainties, and coarse observable binning, which can hinder discrimination among competing beyond-the-Standard-Model (BSM) explanations of anomalous signals. In…
We investigate the capability of the DUNE Near Detector (ND) to constrain Non Standard Interaction parameters (NSI) describing the production of neutrinos ($\varepsilon_{\alpha\beta}^s$) and their detection ($\varepsilon_{\alpha\beta}^d$).…
This work uses a combination of a variational auto-encoder and generative adversarial network to compare different dark energy models in light of observations, e.g., the distance modulus from type Ia supernovae. The network finds an…
Detection of sub-GeV dark matter (DM) particles in direct detection experiments is inherently difficult, as their low kinetic energies in the galactic halo are insufficient to produce observable recoils of the heavy nuclei in the detectors.…
Spurred on by recent successes in causal inference competitions, Bayesian nonparametric (and high-dimensional) methods have recently seen increased attention in the causal inference literature. In this paper, we present a comprehensive…
We compute the neutralino direct detection rate in non-standard cosmological scenarios where neutralinos account for the dark matter of the Universe. Significant differences are found when such rates are compared with those predicted by the…
Many Bayesian inference problems involve high dimensional models for which only a subset of the model variables are of actual interest. All other variables are just nuisance parameters that one would ideally like to integrate out…
The technical breakthroughs of multiple detectors developed by Daya Bay and RENO collaborations have gotten great attention. Yet the optimal determination of neutrino mixing parameters from reactor data depends on the statistical method and…