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Oscillation analysis with NuPRISM

2017· article· en· W2760415162 on OpenAlexaff
M. Scott

Bibliographic record

VenueJournal of Physics Conference Series · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsTRIUMF
Fundersnot available
KeywordsPhysicsNeutrino oscillationNeutrinoMeasurements of neutrino speedFlux (metallurgy)Oscillation (cell signaling)MuonNuclear physicsDetectorParticle physicsCherenkov radiationSolar neutrino problemMuon neutrinoBeam (structure)Neutrino detectorSolar neutrinoOptics

Abstract

fetched live from OpenAlex

NuPRISM is a proposed intermediate water Cherenkov detector for the T2K and Hyper-K long baseline neutrino oscillation experiments. The detector makes use of the off-axis effect, where the peak energy of the neutrino flux falls with increasing perpendicular distance from the neutrino beam axis. By spanning the 1 – 4 degree off-axis angular range NuPRISM can sample neutrino fluxes with peak energies from 1200 to 400 MeV respectively. These samples can be linearly combined to create an effective neutrino flux, such as the muon neutrino flux at the far detector for some choice of the neutrino oscillation parameters. This proceedings presents the NuPRISM muon neutrino disappearance analysis, showing that this technique is unaffected by mis-modelled neutrino cross-sections. It also presents the electron neutrino appearance analysis, showing the preliminary uncertainty on a measurement of the cross-section ratio at NuPRISM.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.309
Teacher spread0.276 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2017
Admission routes1
Has abstractyes

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