相关论文: Fast Bayesian Inference for Neutrino Non-Standard …
The direct detection of cosmic neutrino background (CNB) has been a longstanding challenge in particle physics, due to its low number density and tiny neutrino masses. In this work, we consider the spectrum of the CNB boosted by cosmic rays…
The next-to-minimal supersymmetric standard model (NMSSM) naturally provides a 125 GeV Higgs boson without the need for large loop corrections from multi-TeV stop quarks. Furthermore, the NMSSM provides an electroweak scale dark matter…
Conventional dark matter direct detection experiments set stringent constraints on dark matter by looking for elastic scattering events between dark matter particles and nuclei in underground detectors. However these constraints weaken…
The non-detection of GeV-scale WIMPs has led to increased interest in more general candidates, including sub-GeV dark matter. Direct detection experiments, despite their high sensitivity to WIMPs, are largely blind to sub-GeV dark matter.…
We explore the impact of nonstandard interactions at source and detector on the interpretation of reactor electron neutrino disappearance experiments with short- and medium-baseline designs. We use the constraints from the recent results…
In the light of the recent WMAP results we update the constraints on a class of non standard BBN models with a simultaneous combination of non standard neutrino distributions and extra effective number of neutrinos in the expansion rate.…
Current neutrino experiments measure the neutrino mixing parameters with an unprecedented accuracy. The upcoming generation of experiments will be sensitive to subdominant effects that can give information on the unknown neutrino…
Bayesian inference promises to ground and improve the performance of deep neural networks. It promises to be robust to overfitting, to simplify the training procedure and the space of hyperparameters, and to provide a calibrated measure of…
While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature. To address this issue, several highly flexible and scalable…
Present and upcoming neutrino experiments can have considerable sensitivity to dark sectors that interact feebly with the Standard Model. We consider dark sectors interacting with the SM through irrelevant portals that are motivated on…
Neural networks appear to have mysterious generalization properties when using parameter counting as a proxy for complexity. Indeed, neural networks often have many more parameters than there are data points, yet still provide good…
We present an overview of supersymmetry searches, both at collider experiments and via searches for dark matter (DM). We focus on three DM possibilities in the SUSY context: the thermally produced neutralino, a mixture of axion and axino,…
We study potential signals of neutralino dark matter indirect detection by neutrino telescopes in a wide range of CMSSM parameters. We also compare with direct detection potential signals taking into account in both cases present and future…
We investigate the possibility of neutrinos interacting with a scalar dark matter field and the resulting implications for neutrino oscillations in the long-baseline sector. As our Universe is predominantly composed of dark matter,…
Understanding the properties of strongly interacting matter at extreme densities is a central problem in fundamental physics, but neutron star mergers provide a natural laboratory for probing this regime. However, the complexity of the…
The increasingly precise neutrino experiments raise the hope for searching for new physics through studying the impact of Neutral Current (NC) Non-Standard Interactions (NSI) of neutrinos with matter fields. Neutrino oscillation experiments…
Inference for mechanistic models is challenging because of nonlinear interactions between model parameters and a lack of identifiability. Here we focus on a specific class of mechanistic models, which we term stable differential equations.…
Cosmological experiments often employ Bayesian workflows to derive constraints on cosmological and astrophysical parameters from their data. It has been shown that these constraints can be combined across different probes such as Planck and…
Neural Posterior Estimation (NPE) enables rapid parameter inference for complex simulators with intractable likelihoods. NPE trains an inference network to estimate a probability density over parameters given data, typically assumed to be…
We review the prospects of detecting supersymmetric dark matter in the framework of the Constrained Minimal Supersymmetric Standard Model, and compare indirect with direct detection capabilities.