Related papers: Fast Bayesian Inference for Neutrino Non-Standard …
Anomaly and similarity detection in multidimensional series have a long history and have found practical usage in many different fields such as medicine, networks, and finance. Anomaly detection is of great appeal for many different…
The present thesis aims to tackle two critical aspects of present and future cosmological analysis of Large-Scale Structure (LSS): accurate modelling of the nonlinear matter power spectrum beyond $\Lambda$CDM, and efficient computational…
In statistical applications, it is common to encounter parameters supported on a varying or unknown dimensional space. Examples include the fused lasso regression, the matrix recovery under an unknown low rank, etc. Despite the ease of…
We study the constraints on neutralino dark matter in minimal low energy supersymmetry models and the case of heavy lepton and quark scalar superpartners. For values of the Higgsino and gaugino mass parameters of the order of the weak…
Due to the growing adoption of deep neural networks in many fields of science and engineering, modeling and estimating their uncertainties has become of primary importance. Despite the growing literature about uncertainty quantification in…
While much supersymmetric weakly interacting massive particle (WIMP) parameter space has been ruled out, one remaining important candidate is Higgsino dark matter. The Higgsino can naturally realize the "inelastic dark matter" scenario,…
Inferring parameters and testing hypotheses from gravitational wave signals is a computationally intensive task central to modern astrophysics. Nested sampling, a Bayesian inference technique, has become an established standard for this in…
We introduce a novel approach that utilizes neutrino events from the off-axis near detector to investigate the beam profile in long-baseline neutrino experiments. Understanding the dynamics of the neutrino beam is crucial for improving the…
We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian…
Underground laboratories host two kind of experiments at the frontier of our knowledge in Particle Physics, Astrophysics and Cosmology: the direct detection of the Dark Matter of the Universe and the search for the Neutrinoless Double Beta…
We propose a method to ease the challenges of exploring multi-dimensional parameter spaces in beyond-the-Standard Model theories. We evaluate the model likelihood for any choice of parameters by sampling the theory parameters intelligently…
Inverse problems and, in particular, inferring unknown or latent parameters from data are ubiquitous in engineering simulations. A predominant viewpoint in identifying unknown parameters is Bayesian inference where both prior information…
We study the influence of the temperature-dependent interaction between dark matter (DM) and neutrinos on the measurement of cosmological parameters. We pay attention to the neutrino mass effects, so that the derivation of Boltzmann…
Bayesian inference for neural networks, or Bayesian deep learning, has the potential to provide well-calibrated predictions with quantified uncertainty and robustness. However, the main hurdle for Bayesian deep learning is its computational…
We explore the detection prospects for a minimal secluded dark matter model, where a fermionic dark matter particle interacts with the Standard Model (SM) via a kinetically mixed dark photon. We focus on scenarios where the dark photon…
In this review article the current status of particle dark matter is addressed. We discuss the main theoretical extensions of the standard model which allow to explain dark matter in terms of a (yet undiscovered) elementary particle. We…
This paper presents the custom implementation, optimization, and performance evaluation of convolutional neural networks on field programmable gate arrays, for the purposes of accelerating deep neural network inference on large,…
Direct detection experiments aim at the detection of dark matter in the form of weakly interacting massive particles (WIMPs) by searching for signals from elastic dark matter nucleus scattering. Additionally, inelastic scattering in which…
Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well calibrated uncertainty. However, scaling Bayesian inference techniques to deep neural networks is…
We review the direct detection of supersymmetric dark matter in the light of recent experimental results. In particular, we show that regions in the parameter space of several supergravity scenarios with a neutralino-nucleon cross section…