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Record W7032666996

Observation of Diffractive <i>J</i>/ψ Production at the Fermilab Tevatron

2001· article· en· W7032666996 on OpenAlexfundno aff

Bibliographic record

VenueLincoln (University of Nebraska) · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaIstituto Nazionale di Fisica NucleareKorea Science and Engineering FoundationNational Science CouncilAlfred P. Sloan FoundationFermilabU.S. Department of EnergySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsTevatronPseudorapidityRapidityMuonGluonFermilabProduction (economics)
DOInot available

Abstract

fetched live from OpenAlex

We report the first observation of diffractive J/ψ(→μ+μ-) production in p̅p collisions at √s= 1.8 TeV. Diffractive events are identified by their rapidity gap signature. In a sample of events with two muons of transverse momentum pT,/sub> μ> 2 GeV/c within the pseudorapidity region |η| J/ψ production rates is found to be RJ/ψ=[1.45 ± 0.25]%. The ratio RJ/ψ(x) is presented as a function of x-Bjorken. By combining it with our previously measured corresponding ratio Rjj(x) for diffractive dijet production, we extract a value of 0.59 ± 0.15 for the gluon fraction of the diffractive structure function of the proton.

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)0.000
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.211
Teacher spread0.202 · 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
Published2001
Admission routes1
Has abstractyes

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Same venueLincoln (University of Nebraska)Same topicMachine Learning in BioinformaticsFrench-language works237,207