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Record W6992696562

Measurement of the Inclusive μ Charged Current Cross Section on Carbon in the Near Detector of the T2K Experiment

2016· article· en· W6992696562 on OpenAlexfundno aff

Bibliographic record

VenueNazarbayev University Repository (Nazarbayev University) · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsnot available
FundersInstitut National de Physique Nucléaire et de Physique des ParticulesDeutsches Elektronen-SynchrotronScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche ScientifiqueMinistry of Education, Culture, Sports, Science and TechnologyMinistry of Education and Science of the Russian FederationMinisterio de Ciencia e InnovaciónCERNDeutsche ForschungsgemeinschaftSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungU.S. Department of EnergyRussian Foundation for Basic ResearchNational Science Foundation
KeywordsCharged currentMuonMonte Carlo methodCross section (physics)DetectorMomentum (technical analysis)NeutrinoScintillatorBeam (structure)
DOInot available

Abstract

fetched live from OpenAlex

T2K has performed the first measurement of νμ inclusive charged current interactions on carbon at
\nneutrino energies of ∼1 GeV where the measurement is reported as a flux-averaged double differential
\ncross section in muon momentum and angle. The flux is predicted by the beam Monte Carlo and
\n3
\nexternal data, including the results from the NA61/SHINE experiment. The data used for this
\nmeasurement were taken in 2010 and 2011, with a total of 10.8 × 1019 protons-on-target. The
\nanalysis is performed on 4485 inclusive charged current interaction candidates selected in the most
\nupstream fine-grained scintillator detector of the near detector. The flux-averaged total cross section
\nis hσCCi = (6.91±0.13(stat)±0.84(syst))×10−39 cm2
\nnucleon for a mean neutrino energy of 0.85 GeV.

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 score0.264
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.013
GPT teacher head0.234
Teacher spread0.220 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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