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Record W4410946629 · doi:10.1103/htfm-tbdq

Enhancing DUNE’s Solar Neutrino Capabilities with Neutral-Current Detection

2025· article· en· W4410946629 on OpenAlexaboutno aff
S. Meighen-Berger, Jayden L. Newstead, J. F. Beacom, Nicole F. Bell, Matthew J. Dolan

Bibliographic record

VenuePhysical Review Letters · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsnot available
FundersAustralian Research CouncilUniversity of MelbourneNational Science Foundation
KeywordsNeutrinoPhysicsFlux (metallurgy)Solar neutrinoElectronParticle physicsNuclear physicsNeutrino oscillationMaterials science

Abstract

fetched live from OpenAlex

We show that the Deep Underground Neutrino Experiment (DUNE) has the potential to make a precise measurement of the total active flux of ^{8}B solar neutrinos via neutral-current (NC) interactions with argon. This would complement proposed precise measurements of solar-neutrino fluxes in DUNE via charged-current (CC) interactions with argon and mixed CC/NC interactions with electrons. Together, these would enable DUNE to make a Sudbury Neutrino Observatory (SNO)-like comparison of rates and thus to make the most precise measurements of sin^{2}θ_{12} and Δm_{21}^{2} using solar neutrinos. Realizing this potential requires dedicated but realistic efforts to improve DUNE's low-energy capabilities and separately to reduce neutrino-argon cross-section uncertainties. Comparison of mixing-parameter results obtained using solar neutrinos in DUNE and reactor antineutrinos in the Jiangmen Underground Neutrino Observatory (JUNO) would allow for unprecedented tests of new physics.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.002
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 head0.308
Teacher spread0.296 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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