相关论文: Treating the b quark distribution function with re…

In the absence of direct evidence for New Physics at present LHC energies, the focus is set on the anomalies and discrepancies recently observed in rare $b \to s\ell\ell$ transitions which can be interpreted as indirect hints. Global fits…

Recent advances in deep learning have led to its widespread adoption across diverse domains, including medical imaging. This progress is driven by increasingly sophisticated model architectures, such as ResNets, Vision Transformers, and…

Estimating heterogeneous treatment effects across individuals has attracted growing attention as a statistical tool for performing critical decision-making. We propose a Bayesian inference framework that quantifies the uncertainty in…

Many practical optimization problems involve uncertain parameters that are strictly positive. However, the most common uncertainty sets used in robust optimization are the box and the ellipsoidal sets, which may include non-positive values…

A shape-function independent relation is derived between the partial B->X_u+l+nu decay rate with a cut on P_+=E_X-P_X<Delta and a weighted integral over the normalized B->X_s+gamma photon-energy spectrum. The leading-power contribution to…

Neural networks make accurate predictions but often fail to provide reliable uncertainty estimates, especially under covariate distribution shifts between training and testing. To address this problem, we propose a Bayesian framework for…

This work sets the non isotropic noncentral elliptical shape distributions via QR decomposition in the context of zonal polynomials, avoiding the invariant polynomials and the open problems for their computation. The new shape distributions…

The hyperfine interactions of the constituent quark model provide a natural explanation for many nucleon properties, including the Delta-N splitting, the charge radius of the neutron, and the observation that the proton's quark distribution…

We provide an experimental and theoretical perspective on the behavior of unpolarized distribution functions for the nucleon and pion on the valence-quark domain; namely, Bjorken-x \gtrsim 0.4. This domain is key to much of hadron physics;…

Deconvolution is a statistical inverse problem to estimate the distribution of a random variable based on its noisy observations. Despite the extensive studies on the topic, deconvolution with unknown noise distribution remains as a…

A new generation of parton distribution functions with increased precision and quantitative estimates of uncertainties is presented. This work includes a full treatment of available experimental correlated systematic errors for both new and…

We investigate the behaviour of the perturbative relation between the photon energy spectrum in B -> Xs gamma and the hadronic P+ spectrum in semileptonic B -> Xu l nu decay at high orders in perturbation theory in the "large-beta_0" limit,…

Using BaBar measurements of the inclusive electron spectrum in B->X_u e nu decays and the inclusive photon spectrum in B->X_s gamma decays, we extract the magnitude of the CKM matrix element Vub. The extraction is based on theoretical…

When applying a Deep Learning model to medical images, it is crucial to estimate the model uncertainty. Voxel-wise uncertainty is a useful visual marker for human experts and could be used to improve the model's voxel-wise output, such as…

We consider the problem of uncertainty quantification for an unknown low-rank matrix $\mathbf{X}$, given a partial and noisy observation of its entries. This quantification of uncertainty is essential for many real-world problems, including…

We study computing geometric problems on uncertain points. An uncertain point is a point that does not have a fixed location, but rather is described by a probability distribution. When these probability distributions are restricted to a…

Uncertainty is a key feature of any machine learning model and is particularly important in neural networks, which tend to be overconfident. This overconfidence is worrying under distribution shifts, where the model performance silently…

In order to determine the ratio of CKM matrix elements |V_{ub}/V_{cb}| (and |V_{ub}|), we propose a new model-independent method based on the heavy quark effective theory, which is theoretically described by the phase space factor and the…

A field theoretic description for inclusive semileptonic B meson decays is formulated. We argue that large regions of the phase spaces for the decays are dominated by distances near the light cone. The light-cone dominance allows to…

Scientific machine learning increasingly uses spectral methods to understand physical systems. Current spectral learning approaches provide only point estimates without uncertainty quantification, limiting their use in safety-critical…