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Record W7111285338 · doi:10.48550/arxiv.2501.18979

A compact frozen-spin trap for the search for the electric dipole moment of the muon

2025· preprint· en· W7111285338 on OpenAlexaff

Bibliographic record

VenueResearch Explorer (The University of Manchester) · 2025
Typepreprint
Languageen
FieldComputer Science
TopicComputational Physics and Python Applications
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsMuonAnomalous magnetic dipole momentMomentum (technical analysis)Electric dipole momentSensitivity (control systems)Solenoidal vector fieldBeamlineDipoleMuon collider

Abstract

fetched live from OpenAlex

The electric dipole moments~(EDM) of fundamental particles inherently violate parity~(P) and time-reversal~(T) symmetries. By virtue of the CPT theorem in quantum field theory, the latter also implies the violation of the combined charge-conjugation and parity~(CP) symmetry. We aim to measure the EDM of the muon using the frozen-spin technique within a compact storage trap. This method exploits the high effective electric field, \$E \approx 165\$ MV/m, experienced in the rest frame of the muon with a momentum of about 23 MeV/c when it passes through a solenoidal magnetic field of \$|\vec{B}|=2.5\$ T. In this paper, we outline the fundamental considerations for a muon EDM search and present a conceptual design for a demonstration experiment to be conducted at secondary muon beamlines of the Paul Scherrer Institute in Switzerland. In Phase~I, with an anticipated data acquisition period of 200 days, the expected sensitivity to a muon EDM is 4E-21 ecm. In a subsequent phase, Phase~II, we propose to improve the sensitivity to 6E-23 ecm using a dedicated instrument installed on a different beamline that produces muons of momentum 125 MeV/c}.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.120
GPT teacher head0.347
Teacher spread0.227 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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