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Record W4386293117 · doi:10.1007/jhep08(2023)181

Constraining Vector Dark Matter with neutrino experiments

2023· article· en· W4386293117 on OpenAlexfundno aff
Dawid Brzemiński, Saurav Das, Anson Hook, Clayton Ristow

Bibliographic record

VenueJournal of High Energy Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsnot available
FundersInstitut Périmètre de physique théoriqueGovernment of CanadaNational Science Foundation
KeywordsPhysicsParticle physicsNeutrinoDark matterParameter spaceLeptonPhysics beyond the Standard ModelNeutrino oscillationSuper-KamiokandeEigenvalues and eigenvectorsSolar neutrinoNuclear physicsQuantum mechanicsGeometry

Abstract

fetched live from OpenAlex

Abstract Vector Dark Matter (VDM) that couples to lepton flavor (Le, Lμ, Lτ) acts similarly to a chemical potential for the neutrino flavor eigenstates and modifies neutrino oscillations. VDM imparts unique signatures such as time and directional dependence with longer baselines giving better sensitivity. We use the non-observation of such a signal at Super-Kamiokande to rule out the existence of VDM in a region of parameter space several orders of magnitude beyond other constraints and show the projected reach of future experiments such as DUNE.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.229
Teacher spread0.218 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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