相关论文: Efficiently Exploring Multi-Dimensional Parameter …
Neutrinos, being elusive subatomic particles having only weak interactions, provide an ideal platform to look for physics beyond the Standard Model. In the present era of neutrino physics, various experiments are focusing towards 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…
Future long-baseline experiments will play an important role in exploring physics beyond the standard model. One such new physics concept is the large extra dimension (LED), which provides an elegant solution to the hierarchy problem. This…
Measuring observables to constrain models using maximum-likelihood estimation is fundamental to many physics experiments. Wilks' theorem provides a simple way to construct confidence intervals on model parameters, but it only applies under…
Monte Carlo Event Generators are important tools for the understanding of physics at particle colliders like the LHC. In order to best predict a wide variety of observables, the optimization of parameters in the Event Generators based on…
The photon spectrum in macrocoherent atomic de-excitation via radiative emission of neutrino pairs (RENP) has been proposed as a sensitive probe of the neutrino mass spectrum, capable of competing with conventional neutrino experiments. In…
We consider a class of minimal anomaly free $\mathrm{U}(1)$ extensions of the Standard Model with three generations of right-handed neutrinos and a complex scalar. Using electroweak precision constraints, new 13 TeV LHC data, and…
Quantum mechanics for many-body systems may be reduced to the evaluation of integrals in 3N dimensions using Monte-Carlo, providing the Quantum Monte Carlo ab initio methods. Here we limit ourselves to expectation values for trial…
In machine learning, likelihood-free inference refers to the task of performing an analysis driven by data instead of an analytical expression. We discuss the application of Neural Spline Flows, a neural density estimation algorithm, to the…
One of purposes for High Energy accelerator experiments is confrontation of theory and measurements in ever new realms. Any new agreement extends theory applicability domain, any discrepancy hints to unexplained. That calls for better…
We study neutrino physics in a model with one large extra dimension. We assume the existence of two four-dimensional branes in the five-dimensional space-time, one for the ordinary particles and the other one for mirror particles, and we…
Methods that bypass analytical evaluations of the likelihood function have become an indispensable tool for statistical inference in many fields of science. These so-called likelihood-free methods rely on accepting and rejecting simulations…
The use of extra dimensional scenarios as models for neutrino mass affects many low energy observables. We consider the implications of virtual bulk neutrinos in precision experiments of the anomalous magnetic moments of the muon and the…
Extrapolations of soft scalar mass parameters in supersymmetric theories can be used to explore elements of the physics scenario near the grand unification scale. We investigate the potential of this method in the lepton sector of SO(10)…
The Standard Model of Particle Physics and its description of Nature have been recently challenged by a series of precision measurements performed via different accelerator machines. Statistically significant anomalies emerged in the heavy…
Eclipsing binaries provide one of the most direct mechanisms for measuring stellar properties such as mass and radius, but historically, determining these properties has been non-trivial and computationally prohibitive. As such, only a…
Psychiatric neuroscience is increasingly aware of the need to define psychopathology in terms of abnormal neural computation. The central tool in this endeavour is the fitting of computational models to behavioural data. The most prominent…
We consider nonlinear mixed effects models including high-dimensional covariates to model individual parameters variability. The objective is to identify relevant covariates among a large set under sparsity assumption and to estimate model…
Investigation of well-motivated parameter space in the theories of Beyond the Standard Model (BSM) plays an important role in new physics discoveries. However, a large-scale exploration of models with multi-parameter or equivalent solutions…
Parameter inference and uncertainty quantification are important steps when relating mathematical models to real-world observations, and when estimating uncertainty in model predictions. However, methods for doing this can be…