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Record W7067679064

Measurement of the electron neutrino charged-current interaction rate on water with the T2K ND280 pi-zero detector

2015· article· en· W7067679064 on OpenAlexfundno aff

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicGerman History and Society
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
KeywordsCherenkov radiationDetectorNeutrinoElectron neutrinoCherenkov detectorBeam (structure)ElectronCathode ray
DOInot available

Abstract

fetched live from OpenAlex

This paper presents a measurement of the charged current interaction rate of the electron neutrino beam component of the beam above $1.5$~GeV using the large fiducial mass of the T2K $\pi^0$ detector. The predominant poriton of the $\nu_e$ flux ($\sim$85 %) at these energies comes from kaon decays. The measured ratio of the observed beam interaction rate to the predicted rate in the detector with water targets filled is 0.89 $\pm$ 0.08 (stat.) $\pm$ 0.11 (sys.), and with the water targets emptied is 0.90 $\pm$ 0.09 (stat.) $\pm$ 0.13 (sys.). The ratio obtained for the interactions on water only from an event subtraction method is 0.87 $\pm$ 0.33 (stat.) $\pm$ 0.21 (sys.). This is the first measurement of the interaction rate of electron neutrinos on water, which is particularly of interest to experiments with water Cherenkov detectors.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.429

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 head0.053
GPT teacher head0.197
Teacher spread0.144 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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