English
Related papers

Related papers: Muon $g$$-$2: correlation-induced uncertainties in…

200 papers

The leading-order hadronic contribution to the muon magnetic moment anomaly $a_\mu\equiv (g_\mu-2)/2$, calculated using a dispersion integral of $e^+e^-$ annihilation data and $\tau$ data, is briefly reviewed. This contribution has the…

High Energy Physics - Phenomenology · Physics 2013-12-31 Zhiqing Zhang

The hadronic contributions to the muon anomalous magnetic moment and to the shift of the electromagnetic fine structure constant at the scale of Z boson mass are evaluated within dispersively improved perturbation theory (DPT). The latter…

High Energy Physics - Phenomenology · Physics 2017-10-24 A. V. Nesterenko

Future high-energy $e^+e^-$ colliders will provide some of the most precise tests of the Standard Model. Statistical uncertainties are expected to improve by orders of magnitude over current measurements. This provides a new challenge in…

High Energy Physics - Experiment · Physics 2021-05-21 Jakob Beyer , Jenny List

Numerous studies have focused on learning and understanding the dynamics of physical systems from video data, such as spatial intelligence. Artificial intelligence requires quantitative assessments of the uncertainty of the model to ensure…

Machine Learning · Computer Science 2024-12-18 Aoming Liang , Qi Liu , Lei Xu , Fahad Sohrab , Weicheng Cui , Changhui Song , Moncef Gabbouj

We review the present status of the Standard Model calculation of the anomalous magnetic moment of the muon. This is performed in a perturbative expansion in the fine-structure constant $\alpha$ and is broken down into pure QED,…

High Energy Physics - Phenomenology · Physics 2020-11-16 T. Aoyama , N. Asmussen , M. Benayoun , J. Bijnens , T. Blum , M. Bruno , I. Caprini , C. M. Carloni Calame , M. Cè , G. Colangelo , F. Curciarello , H. Czyż , I. Danilkin , M. Davier , C. T. H. Davies , M. Della Morte , S. I. Eidelman , A. X. El-Khadra , A. Gérardin , D. Giusti , M. Golterman , Steven Gottlieb , V. Gülpers , F. Hagelstein , M. Hayakawa , G. Herdoíza , D. W. Hertzog , A. Hoecker , M. Hoferichter , B. -L. Hoid , R. J. Hudspith , F. Ignatov , T. Izubuchi , F. Jegerlehner , L. Jin , A. Keshavarzi , T. Kinoshita , B. Kubis , A. Kupich , A. Kupść , L. Laub , C. Lehner , L. Lellouch , I. Logashenko , B. Malaescu , K. Maltman , M. K. Marinković , P. Masjuan , A. S. Meyer , H. B. Meyer , T. Mibe , K. Miura , S. E. Müller , M. Nio , D. Nomura , A. Nyffeler , V. Pascalutsa , M. Passera , E. Perez del Rio , S. Peris , A. Portelli , M. Procura , C. F. Redmer , B. L. Roberts , P. Sánchez-Puertas , S. Serednyakov , B. Shwartz , S. Simula , D. Stöckinger , H. Stöckinger-Kim , P. Stoffer , T. Teubner , R. Van de Water , M. Vanderhaeghen , G. Venanzoni , G. von Hippel , H. Wittig , Z. Zhang , M. N. Achasov , A. Bashir , N. Cardoso , B. Chakraborty , E. -H. Chao , J. Charles , A. Crivellin , O. Deineka , A. Denig , C. DeTar , C. A. Dominguez , A. E. Dorokhov , V. P. Druzhinin , G. Eichmann , M. Fael , C. S. Fischer , E. Gámiz , Z. Gelzer , J. R. Green , S. Guellati-Khelifa , D. Hatton , N. Hermansson-Truedsson , S. Holz , B. Hörz , M. Knecht , J. Koponen , A. S. Kronfeld , J. Laiho , S. Leupold , P. B. Mackenzie , W. J. Marciano , C. McNeile , D. Mohler , J. Monnard , E. T. Neil , A. V. Nesterenko , K. Ottnad , V. Pauk , A. E. Radzhabov , E. de Rafael , K. Raya , A. Risch , A. Rodríguez-Sánchez , P. Roig , T. San José , E. P. Solodov , R. Sugar , K. Yu. Todyshev , A. Vainshtein , A. Vaquero Avilés-Casco , E. Weil , J. Wilhelm , R. Williams , A. S. Zhevlakov

A major problem in numerical weather prediction (NWP) is the estimation of high-dimensional covariance matrices from a small number of samples. Maximum likelihood estimators cannot provide reliable estimates when the overall dimension is…

Methodology · Statistics 2023-01-13 Robert J. Webber , Matthias Morzfeld

Complex engineered systems require coordinated design choices across heterogeneous components under multiple conflicting objectives and uncertain specifications. Monotone co-design provides a compositional framework for such problems by…

Optimization and Control · Mathematics 2026-03-20 Yujun Huang , Gioele Zardini

We present a calculation of the hadronic vacuum polarization contribution to the muon anomalous magnetic moment, $a_\mu^{\mathrm hvp}$, in lattice QCD employing dynamical up and down quarks. We focus on controlling the infrared regime of…

High Energy Physics - Lattice · Physics 2017-10-11 M. Della Morte , A. Francis , V. Gülpers , G. Herdoíza , G. von Hippel , H. Horch , B. Jäger , H. B. Meyer , A. Nyffeler , H. Wittig

I review the recent efforts to improve the precision of the prediction of the anomalous moment of the muon, in particular of the hadronic contribution of the vacuum polarization, which is the contribution with the largest uncertainty. Focus…

High Energy Physics - Experiment · Physics 2009-10-20 D. Bernard

In the context of a recent CTEQ6.6 global analysis, we review a new technique for studying correlated theoretical uncertainties in hadronic observables associated with imperfect knowledge of parton distribution functions (PDFs). The…

High Energy Physics - Phenomenology · Physics 2008-09-08 Pavel M. Nadolsky

We present a comprehensive analytical study of a variation of the eigenvector ensemble initially proposed by Deutsch for the foundations of the Eigenstate Thermalization Hypothesis (ETH). This ensemble, called the $C$-ensemble, incorporates…

Quantum Physics · Physics 2025-06-27 William E. Salazar , Juan Diego Urbina , Javier Madroñero

Gaussian process regression (GPR) has been a well-known machine learning method for various applications such as uncertainty quantifications (UQ). However, GPR is inherently a data-driven method, which requires sufficiently large dataset.…

Machine Learning · Computer Science 2023-05-03 Cheng Chang , Tieyong Zeng

This paper introduces a novel and scalable framework for uncertainty estimation and separation with applications in data driven modeling in science and engineering tasks where reliable uncertainty quantification is critical. Leveraging an…

Machine Learning · Computer Science 2024-12-19 Navid Ansari , Hans-Peter Seidel , Vahid Babaei

The Gamma Variance Model (GVM) is a statistical model that incorporates uncertainties in the assignment of systematic errors (informally called errors-on-errors). The model is of particular use in analyses that combine the results of…

High Energy Physics - Experiment · Physics 2025-07-04 Enzo Canonero , Glen Cowan

Electromagnetic corrections to hadronic vacuum polarization contribute significantly to the uncertainty of the Standard Model prediction of the muon anomaly, which poses conceptual and numerical challenges for ab initio lattice…

High Energy Physics - Lattice · Physics 2025-06-25 A. Altherr , I. Campos , A. Cotellucci , R. Gruber , T. Harris , J. Komijani , F. Margari , M. K. Marinkovic , L. Parato , A. Patella , S. Rosso , N. Tantalo , P. Tavella

This paper presents a simplified likelihood framework designed to facilitate the reuse, reinterpretation and combination of LHC experimental results. The framework is based on the same underlying structure as the widely used HistFactory…

High Energy Physics - Experiment · Physics 2023-05-18 Nicolas Berger

We compare the isospin-one, vector-current hadronic vacuum polarization (HVP) obtained from isospin-symmetric lattice QCD with that obtained from a dispersive representation employing inclusive hadronic $\tau$ decay data corrected for…

High Energy Physics - Phenomenology · Physics 2026-05-13 Noah Allen , Diogo Boito , Maarten Golterman , Kim Maltman , Lucas M. Mansur , Santiago Peris

We present an alternative way to determine the unknown parameter associated to a gaussian approximation in a generic two-dimensional model. Instead of the standard variational approach, we propose a procedure based on a quantitative…

High Energy Physics - Theory · Physics 2016-08-16 Aníbal Iucci , Carlos Naón

We consider the goal of predicting how complex networks respond to chronic (press) perturbations when characterizations of their network topology and interaction strengths are associated with uncertainty. Our primary result is the…

Populations and Evolution · Quantitative Biology 2016-10-26 David Koslicki , Mark Novak

We present a summary of the results of two recent precise calculations of the muon anomalous magnetic moment ($g_{\mu}-2$) and the electromagnetic coupling on the $Z$ ($\bar{\alpha}_{\rm Q.E.D.}(M^2_{Z})$). The main sources of uncertainty…

High Energy Physics - Phenomenology · Physics 2007-05-23 J. F. de Trocóniz , F. J. Ynduráin