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Record W3163302669 · doi:10.1007/jhep04(2022)174

Heavy neutrino searches through double-bang events at Super-Kamiokande, DUNE, and Hyper-Kamiokande

2022· article· en· W3163302669 on OpenAlexafffund
M. C. Atkinson, Pilar Coloma, Ivan Martínez-Soler, Noemi Rocco, Ian M. Shoemaker

Bibliographic record

VenueJournal of High Energy Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsTRIUMF
FundersAgencia Estatal de InvestigaciónOffice of ScienceEuropean CommissionTRIUMFEuropean Social FundHigh Energy PhysicsU.S. Department of Energy
KeywordsPhysicsSuper-KamiokandeNeutrinoParticle physicsLeptonNeutrino oscillationNuclear physicsFlux (metallurgy)Solar neutrinoMeasurements of neutrino speedPhenomenology (philosophy)Electron

Abstract

fetched live from OpenAlex

Abstract A variety of new physics scenarios allows for neutrinos to up-scatter into a heavy neutral lepton state. For a range of couplings and neutrino energies, the heavy neutrino may travel some distance before decaying to visible final states. When both the up-scattering and decay occur within the detector volume, these “double bang” events produce distinctive phenomenology with very low background. In this work, we first consider the current sensitivity at Super-Kamiokande via the atmospheric neutrino flux, and find current data may already provide new constraints. We then examine projected future sensitivity at DUNE and Hyper-Kamiokande, including both atmospheric and beam flux contributions to double-bang signals.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.260
Teacher spread0.236 · 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
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

Citations32
Published2022
Admission routes2
Has abstractyes

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