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Record W4398157374 · doi:10.48550/arxiv.2405.10806

Measurement of hadronic cross sections via initial state radiation at BABAR

2024· preprint· en· W4398157374 on OpenAlexfundno aff
Léonard Polat

Bibliographic record

VenuearXiv (Cornell University) · 2024
Typepreprint
Languageen
FieldComputer Science
TopicComputational Physics and Python Applications
Canadian institutionsnot available
FundersSorbonne UniversitéCentre National de la Recherche ScientifiqueSLAC National Accelerator LaboratoryCERNInstitut National de Physique Nucléaire et de Physique des ParticulesAgence Nationale de la RechercheUniversity of Victoria
KeywordsPhysicsAnomalous magnetic dipole momentMuonParticle physicsHadronNuclear physicsMonte Carlo methodRadiationPhotonVacuum polarizationStatisticsOptics

Abstract

fetched live from OpenAlex

The BABAR experiment participates to the global endeavor for a precise prediction of the anomalous magnetic moment of the muon by evaluating the contribution of hadronic processes to the vacuum polarization. After its last result published in 2009 and 2012, BABAR is preparing a new independent measurement of the $e^+e^- \rightarrow π^+π^-(γ)$ cross section via initial state radiation, with full data statistics and improved uncertainties. A first milestone was reached with the recent study of additional radiations in $e^+e^- \rightarrow π^+π^-(γ)$ and $e^+e^- \rightarrow μ^+μ^-(γ)$, which uncovered shortcomings of the Phokhara Monte Carlo generator in one-photon rates and angular distributions. This has practically no effect on the previous BABAR measurement, but could explain longstanding discrepancies with other experiments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.004

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 head0.067
GPT teacher head0.222
Teacher spread0.154 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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