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Record W7029919415

Measurement of the νμ charged-current quasielastic cross section on carbon with the ND280 detector at T2K

2015· article· en· W7029919415 on OpenAlexfundno aff

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2015
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 CanadaJapan Society for the Promotion of ScienceRussian Foundation for Basic ResearchRussian Science FoundationMinistry of Education, Culture, Sports, Science and TechnologyCompute CanadaCentre National de la Recherche ScientifiqueWestern Canada Research GridCERNDeutsche ForschungsgemeinschaftSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungEuropean Regional Development FundU.S. Department of Energy
KeywordsCross section (physics)Muon neutrinoMomentum (technical analysis)DetectorMuonNeutrinoBeam (structure)
DOInot available

Abstract

fetched live from OpenAlex

<p style="text-align:justify;"> This paper reports a measurement by the T2K experiment of the ν μ charged current quasielastic (CCQE) cross section on a carbon target with the off-axis detector based on the observed distribution of muon momentum ( p μ ) and angle with respect to the incident neutrino beam ( θ μ ). The flux-integrated CCQE cross section was measured to be ⟨ σ ⟩ = ( 0.83 ± 0.12 ) × 10 − 38 cm 2 . The energy dependence of the CCQE cross section is also reported. The axial mass, M QE A , of the dipole axial form factor was extracted assuming the Smith-Moniz CCQE model with a relativistic Fermi gas nuclear model. Using the absolute (shape-only) p μ − cos θ μ distribution, the effective M QE A parameter was measured to be 1.2 6 + 0.21 − 0.18 GeV / c 2 ( 1.4 3 + 0.28 − 0.22 GeV / c 2 ).</p>

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.045
GPT teacher head0.270
Teacher spread0.225 · 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 designObservational
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

Citations1
Published2015
Admission routes1
Has abstractyes

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