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Record W7071478196

Search for heavy neutrinos with the T2K near detector ND280

2019· article· en· W7071478196 on OpenAlexfundno aff

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2019
Typearticle
Languageen
FieldComputer Science
TopicQR Code Applications and Technologies
Canadian institutionsnot available
FundersInstitut National de Physique Nucléaire et de Physique des ParticulesDeutsches Elektronen-SynchrotronScience and Technology Facilities CouncilJapan Society for the Promotion of ScienceHorizon 2020 Framework ProgrammeRussian Foundation for Basic ResearchRussian Science FoundationMinistry of Education, Culture, Sports, Science and TechnologyEuropean Regional Development FundU.S. Department of EnergyEuropean CommissionCentre National de la Recherche ScientifiqueWestern Canada Research GridDeutsche ForschungsgemeinschaftCERNCompute CanadaNatural Sciences and Engineering Research Council of CanadaAlfred P. Sloan FoundationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsNeutrinoDetectorNeutrino oscillationMassless particleRange (aeronautics)Mixing (physics)Neutrino detectorBeam (structure)
DOInot available

Abstract

fetched live from OpenAlex

This paper reports on the search for heavy neutrinos with masses in the range 140<MN<493 MeV/c2 using the off-axis near detector ND280 of the T2K experiment. These particles can be produced from kaon decays in the standard neutrino beam and then subsequently decay in ND280. The decay modes under consideration are N→ℓα±π and N→ℓα+ℓβ-ν(-)(α,β=e,μ). A search for such events has been made using the Time Projection Chambers of ND280, where the background has been reduced to less than two events in the current dataset in all channels. No excess has been observed in the signal region. A combined Bayesian statistical approach has been applied to extract upper limits on the mixing elements of heavy neutrinos to electron-, muon- and tau- flavored currents (Ue2, Uμ2, Uτ2) as a function of the heavy neutrino mass, e.g., Ue2<10-9 at 90% C.L. for a mass of 390 MeV/c2. These constraints are competitive with previous 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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.940

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.017
GPT teacher head0.205
Teacher spread0.188 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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