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Record W4225738811 · doi:10.1103/physrevd.105.075026

Probing neutrino-portal dark matter at the Forward Physics Facility

2022· article· en· W4225738811 on OpenAlexaff
Kevin J. Kelly, Felix Kling, Douglas Tuckler, Yue Zhang

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsCarleton University
FundersFermilabU.S. Department of EnergyFundação Arthur BernardesHigh Energy PhysicsOffice of ScienceDeutsche Forschungsgemeinschaft
KeywordsPhysicsNeutrinoDark matterParticle physicsLarge Hadron ColliderNeutrino detectorPhysics beyond the Standard ModelMeasurements of neutrino speedSterile neutrinoNuclear physicsMissing energyNeutrino oscillationSolar neutrinoLepton

Abstract

fetched live from OpenAlex

The Forward Physics Facility (FPF), planned to operate near the ATLAS interaction point at the LHC, offers exciting new terrain to explore neutrino properties at TeV energy scales. It will reach an unprecedented regime for terrestrial neutrino experiments and provide the opportunity to reveal new physics of neutrinos at higher energy scales. We demonstrate that future detectors at the FPF have the potential to discover new mediators that couple predominantly to neutrinos, with masses between 0.3 GeV and 20 GeV and small couplings not yet probed by existing searches. Such a neutrinophilic mediator is well motivated for addressing the origin of several neutrino-portal dark matter candidates, including thermal freeze-out and sterile-neutrino dark matter scenarios. Experimentally, the corresponding signatures include neutrino charged-current scattering events associated with large missing transverse momentum, and excessive apparent tau-neutrino events. We discuss the FPF detector capabilities needed for this search, most importantly the hadronic energy resolution.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
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 head0.017
GPT teacher head0.376
Teacher spread0.359 · 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

Citations31
Published2022
Admission routes1
Has abstractyes

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