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Record W4406863287 · doi:10.1103/physrevd.111.015029

Neutrino electromagnetic properties and the weak mixing angle at the LHC Forward Physics Facility

2025· article· en· W4406863287 on OpenAlexafffund
Roshan Mammen Abraham, Saeid Foroughi-Abari, Felix Kling, Yu-Dai Tsai

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaSimons FoundationDeutsche ForschungsgemeinschaftU.S. Department of EnergyNational Science Foundation
KeywordsPhysicsNeutrinoParticle physicsNuclear physicsMuonElectron neutrinoLarge Hadron ColliderMeasurements of neutrino speedPhysics beyond the Standard ModelCharge radiusSolar neutrinoNeutrino oscillationElectronSolar neutrino problemRecoilFlux (metallurgy)Neutrino detectorMixing (physics)Tau neutrinoAnomalous magnetic dipole momentProton

Abstract

fetched live from OpenAlex

The LHC produces an intense beam of highly energetic neutrinos of all three flavors in the forward direction, and the Forward Physics Facility (FPF) has been proposed to house a suite of experiments taking advantage of this opportunity. In this study, we investigate the FPF’s potential to probe the neutrino electromagnetic properties, including neutrino millicharge, magnetic moment, and charge radius. We find that, due to the large flux of tau neutrinos at the LHC, the FPF detectors will be able to provide more sensitive constraints on the tau neutrino magnetic moment and millicharge than previous measurements at DONUT, by searching for excess in low recoil energy electron scattering events. We also find that, by precisely measuring the rate of neutral current deep inelastic scattering events, the FPF detectors have the potential to obtain the strongest experimental bounds on the neutrino charge radius for the electron neutrino, and one of the leading bounds for the muon neutrino flavor. The same signature could also be used to measure the weak mixing angle, and we estimate that <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mrow> <a:msup> <a:mrow> <a:mi>sin</a:mi> </a:mrow> <a:mrow> <a:mn>2</a:mn> </a:mrow> </a:msup> <a:msub> <a:mrow> <a:mi>θ</a:mi> </a:mrow> <a:mrow> <a:mi>W</a:mi> </a:mrow> </a:msub> </a:mrow> </a:math> could be measured to about 3% precision at a scale <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:mi>Q</c:mi> <c:mo>∼</c:mo> <c:mn>10</c:mn> <c:mtext> </c:mtext> <c:mtext> </c:mtext> <c:mi>GeV</c:mi> </c:math> , shedding new light on the longstanding NuTeV anomaly.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.321
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.023
GPT teacher head0.387
Teacher spread0.363 · 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 teacher head, not a consensus.

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

Citations10
Published2025
Admission routes2
Has abstractyes

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