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Toward resolving the discrepancy in helium-3 and helium-4 nuclear charge radii

2025· article· en· W4409644194 on OpenAlexafffund
Xiao-Qiu Qi, Pei-Pei Zhang, Zong-Chao Yan, Li-Yan Tang, Aixi Chen, Ting-Yun Shi, Zhen-Xiang Zhong

Bibliographic record

VenuePhysical Review Research · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsUniversity of New Brunswick
FundersZhejiang Sci-Tech UniversityNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsHeliumCharge (physics)PhysicsAtomic physicsNuclear physicsEffective nuclear chargeElectronQuantum mechanics

Abstract

fetched live from OpenAlex

The discrepancy in the squared nuclear charge radius difference, Δ R 2 , between He 3 and He 4 , as determined from electronic and muonic atom energy levels, presents an unresolved puzzle. This paper shows that accounting for off-diagonal hyperfine mixing effects can substantially reduce this discrepancy. We find that hyperfine mixing with the n 3 S and n 1 S states ( n > 2 ) in He 3 introduces a correction of − 1.37 kHz to the isotope shift of the 2 1 S − 2 3 S transition, a factor of seven times larger than the current uncertainty. This correction modifies Δ R 2 by − 0.0064 fm 2 , shifting it from 1.0757 ( 15 ) fm 2 to 1.0693 ( 15 ) fm 2 , as initially reported by Werf []. This brings Δ R 2 closer to the value of 1.0636 ( 31 ) fm 2 obtained from muonic helium μ He + by Schuhmann [], narrowing the existing discrepancy from 3.6 σ to 1.7 σ . The adjusted value Δ R 2 also agrees well with the result of 1.069(3) fm 2 derived from the helium 2 3 S

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)0.009
metaresearch head score (Gemma)0.037
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: none
Teacher disagreement score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.037
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0040.007
Open science0.0040.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0050.004

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 head0.053
GPT teacher head0.419
Teacher spread0.366 · 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

Citations11
Published2025
Admission routes2
Has abstractyes

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