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Precision measurements of muonium and muonic helium hyperfine structure at J-PARC

2025· article· en· W4408460476 on OpenAlexaff
P. Strasser, Mitsushi Abe, Kota Asai, Seiso Fukumura, Mahiro Fushihara, Yu Goto, Takashi Ino, R. Iwai, S. Kanda, Shiori Kawamura, Masaaki Kitaguchi, S. Nishimura, Takayuki Oku, Takuya Okudaira, A.D. Powell., K. Sasaki, Hirohiko M. Shimizu, K. Shimomura, Hiroki Tada, Hiroyuki A. Torii, Takashi Yamanaka, T. Yamazaki

Bibliographic record

VenueThe European Physical Journal D · 2025
Typearticle
Languageen
FieldEngineering
TopicMuon and positron interactions and applications
Canadian institutionsUniversity of Calgary
FundersJapan Society for the Promotion of ScienceMaterials and Life Science Experimental Facility
KeywordsMuoniumHyperfine structureAtomic physicsHeliumMuonPhysicsNuclear physicsJ-PARCOptics

Abstract

fetched live from OpenAlex

Abstract At the J-PARC Muon Science Facility (MUSE), the MuSEUM collaboration is now performing new precision measurements of the ground state hyperfine structure (HFS) of both muonium and muonic helium atoms. High-precision measurements of the muonium ground-state HFS are recognized as one of the most sensitive tools for testing bound-state quantum electrodynamics theory to precisely probe the standard model and determine fundamental constants of the positive muon magnetic moment and mass. The same technique can also be employed to measure muonic helium HFS, obtain the negative muon magnetic moment and mass, and test and improve the theory of the three-body atomic system. Measurements at zero magnetic field have already yielded more accurate results than previous experiments for both muonium and muonic helium atoms. High-field measurements are now ready to start collecting data using the world’s most intense pulsed muon beam at the MUSE H-line. We aim to improve the precision of previous measurements ten times for muonium and a hundred times or more for muonic helium. We review all the key developments for these new measurements, focusing on the high-field experiment, and report the latest results and prospects.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.247
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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