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Alexander Keshavarzi

Researcher at University of Mississippi

Publications -  12
Citations -  965

Alexander Keshavarzi is an academic researcher from University of Mississippi. The author has contributed to research in topics: Muon & Anomalous magnetic dipole moment. The author has an hindex of 6, co-authored 9 publications receiving 555 citations. Previous affiliations of Alexander Keshavarzi include University of Manchester & University of Liverpool.

Papers
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Muon g − 2 and α ( M Z 2 ) : A new data-based analysis

TL;DR: In this paper, the authors presented a complete reevaluation of the hadronic vacuum polarization contributions to the anomalous magnetic moment of the muon, aμhad,VP, and Δαhad(MZ2), from the combination of e+e-→hadrons cross section data.
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g − 2 of charged leptons, α ( M Z 2 ) , and the hyperfine splitting of muonium

TL;DR: In this paper, the authors presented reevaluations of the hadronic vacuum polarization contributions to the anomalous magnetic moments of the electron, muon and tau lepton, to the ground-state hyperfine splitting of muonium and also to the running of the QED coupling at the mass scale of the Z$ boson.
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Data-driven evaluations of Euclidean windows to scrutinize hadronic vacuum polarization

TL;DR: In this article , windows in Euclidean time are used to isolate the origin of potential conflicts between evaluations of the hadronic-vacuum-polarization (HVP) contribution to the anomalous magnetic moment of the muon in lattice QCD and from e+e−→hadrons cross-section data.
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Strong coupling from e + e - →hadrons below charm

TL;DR: In this paper, a new compilation of the hadronic $R$-ratio from available data for the process was used to determine the strong coupling, and the results were in good agreement with a recent determination from the OPAL and ALEPH data for hadronic $\ensuremath{\tau}$ decays.
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g − 2 of the muon: status report

TL;DR: A short review of the status of the anomalous magnetic moment of the muon, g − 2, is given in this paper, with emphasis on recent developments in different sectors and the accuracy of the Standard Model prediction.