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Calculation of the Hadronic Vacuum Polarization Disconnected Contribution to the Muon Anomalous Magnetic Moment

2016· article· en· W2281099383 on OpenAlexafffund
Thomas Blum, P. A. Boyle, Taku Izubuchi, Luchang Jin, Andreas Jüttner, Christoph Lehner, Kim Maltman, Marina Krstić Marinković, Antonin Portelli, Matthew Spraggs

Bibliographic record

VenuePhysical Review Letters · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsYork University
FundersArgonne National LaboratoryEuropean Research CouncilNatural Sciences and Engineering Research Council of CanadaScience and Technology Facilities CouncilOffice of ScienceEngineering and Physical Sciences Research CouncilFermilabSeventh Framework ProgrammeU.S. Department of Energy
KeywordsAnomalous magnetic dipole momentPhysicsVacuum polarizationMuonHadronPionParticle physicsMagnetic momentPolarization (electrochemistry)Quantum chromodynamicsLattice (music)Lattice QCDNuclear physicsCondensed matter physics

Abstract

fetched live from OpenAlex

We report the first lattice QCD calculation of the hadronic vacuum polarization (HVP) disconnected contribution to the muon anomalous magnetic moment at physical pion mass. The calculation uses a refined noise-reduction technique that enables the control of statistical uncertainties at the desired level with modest computational effort. Measurements were performed on the ${48}^{3}\ifmmode\times\else\texttimes\fi{}96$ physical-pion-mass lattice generated by the RBC and UKQCD Collaborations. We find the leading-order hadronic vacuum polarization ${a}_{\ensuremath{\mu}}^{\mathrm{HVP}(\mathrm{LO})\text{disc}}=\ensuremath{-}9.6(3.3)(2.3)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}10}$, where the first error is statistical and the second systematic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.255
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations85
Published2016
Admission routes2
Has abstractyes

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