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Record W2792237257 · doi:10.1103/physrevd.98.115015

Dipole portal to heavy neutral leptons

2018· article· en· W2792237257 on OpenAlexafffund
Gabriel Magill, Ryan Plestid, Maxim Pospelov, Yu-Dai Tsai

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsUniversity of VictoriaMcMaster UniversityPerimeter Institute
FundersFermilabInstitut Périmètre de physique théoriqueNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsMiniBooNEPhysicsNeutrinoParticle physicsFermilabLeptonNuclear physicsBig Bang nucleosynthesisDipoleBeam dumpNeutrino oscillationSterile neutrinoBeam (structure)NucleosynthesisElectron

Abstract

fetched live from OpenAlex

We consider generic neutrino dipole portals between left-handed neutrinos, photons, and right-handed heavy neutral leptons (HNL) with Dirac masses. The dominance of this portal significantly alters the conventional phenomenology of HNLs. We derive a comprehensive set of constraints on the dipole portal to HNLs by utilizing data from LEP, LHC, MiniBooNE, LSND as well as observations of Supernova 1987A and consistency of the standard big bang nucleosynthesis. We calculate projected sensitivities from the proposed high-intensity SHiP beam dump experiment, and the ongoing experiments at the Short-Baseline Neutrino facility at Fermilab. Dipole mediated Primakoff neutrino upscattering and Dalitz-like meson decays are found to be the main production mechanisms in most of the parametric regime under consideration. Proposed explanations of LSND and MiniBooNE anomalies based on HNLs with dipole-induced decays are found to be severely constrained, or to be tested in the future 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 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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.425
Teacher spread0.409 · 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 designTheoretical or conceptual
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

Citations168
Published2018
Admission routes2
Has abstractyes

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