Related papers: Muon $g$$-$2: correlation-induced uncertainties in…
Uncertainty quantification plays an important role in achieving trustworthy and reliable learning-based computational imaging. Recent advances in generative modeling and Bayesian neural networks have enabled the development of…
This paper tackles the problem of robust covariance matrix estimation when the data is incomplete. Classical statistical estimation methodologies are usually built upon the Gaussian assumption, whereas existing robust estimation ones assume…
The hadronic vacuum polarization (HVP) contributions to the muon $g$$-$$2$ are the crucial quantity to resolve whether new physics is present or not in the comparison between the Standard Model (SM) prediction and experimental measurements…
We show that the method of partial covariance is a very efficient way to introduce constraints (such as the centrality selection) in data analysis in ultra-relativistic nuclear collisions. The technique eliminates spurious event-by-event…
Theoretical calculations of the hadronic contribution to the muon anomalous magnetic moment utilize experimental data from e+e- annhilation and tau decay. The data provide input to a dispersion relation. I contend that it is not possible to…
Using recently published, high-precision pi+pi- cross section data by the BABAR experiment from the analysis of e+e- events with high-energy photon radiation in the initial state, we reevaluate the lowest order hadronic contribution…
Experimental errors are now incredibly precise, and are often dominated by the systematic uncertainties. Therefore the errors obtained in the Parton Distribution Functions that are extracted from this data will also be dominated by these…
An accurate determination of the leading-order hadronic vacuum polarisation (HVP) contribution to the anomalous magnetic moment of the muon is critical to understanding the size and significance of any discrepancy between the Standard Model…
We present a new lattice QCD calculation of the leading order hadronic vacuum polarization (LO-HVP) contribution to the muon anomalous magnetic moment $a_\mu$. We reduce uncertainties compared to our earlier computation arXiv:2002.12347 by…
In frames of agreement to consider the annihilation of electron-positron pair to hadrons cross section to be including the virtual photon polarization effects a new formulation of hadron contribution to muon anomalous magnetic moment is…
Conformal prediction, a post-hoc, distribution-free, finite-sample method of uncertainty quantification that offers formal coverage guarantees under the assumption of data exchangeability. Unfortunately, the resulting uncertainty regions…
Optimizing the design of complex systems requires navigating interdependent decisions, heterogeneous components, and multiple objectives. Our monotone theory of co-design offers a compositional framework for addressing this challenge,…
In this talk I present the current status of a precise first-principles calculation of the quark connected, quark disconnected, and leading QED and strong isospin-breaking contributions to the leading-order hadronic vacuum polarization by…
The efforts to improve on the precision of the measurement and theoretical prediction of the anomalous magnetic moment of the muon a_mu have turned into a test of our understanding of the hadronic contribution to vacuum polarisation. I…
At low energies hadronic vacuum polarization (HVP) is strongly dominated by two-pion intermediate states, which are responsible for about $70\%$ of the HVP contribution to the anomalous magnetic moment of the muon, $a_\mu^\text{HVP}$.…
It has been proposed that complex populations, such as those that arise in genomics studies, may exhibit dependencies among observations as well as among variables. This gives rise to the challenging problem of analyzing unreplicated…
Anomalous magnetic moment of the muon (muon g-2) is one of the most precisely measured quantities in particle physics. At the same time, it can be evaluated in the Standard Model with an unprecedented accuracy. The Muon g-2 experiment at…
Most estimators collapse all uncertainty modes into a single confidence score, preventing reliable reasoning about when to allocate more compute or adjust inference. We introduce Uncertainty-Guided Inference-Time Selection, a lightweight…
Regression tasks, notably in safety-critical domains, require proper uncertainty quantification, yet the literature remains largely classification-focused. In this light, we introduce a family of measures for total, aleatoric, and epistemic…
We develop a systematic information-theoretic framework for quantification and mitigation of error in probabilistic Lagrangian (i.e., path-based) predictions which are obtained from dynamical systems generated by uncertain (Eulerian) vector…