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Record W4362503615 · doi:10.1051/epjconf/202328201015

Estimated performance of the TRIUMF ultracold neutron source and electric dipole moment apparatus

2023· article· en· W4362503615 on OpenAlexaffabout
S. Sidhu, Wolfgang Schreyer, S. Vanbergen, S. Kawasaki, R. Matsumiya, Takahiro Okamura, R. Picker

Bibliographic record

VenueEPJ Web of Conferences · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of British ColumbiaSimon Fraser UniversityTRIUMF
Fundersnot available
KeywordsNeutron electric dipole momentUltracold neutronsPhysicsNuclear physicsNeutronElectric dipole momentDipoleSuperfluid helium-4Physics beyond the Standard ModelSpallationSensitivity (control systems)Nuclear engineeringAtomic physicsHeliumQuantum mechanicsEngineering

Abstract

fetched live from OpenAlex

Searches for the permanent electric dipole moment of the neutron (nEDM) provide strong constraints on theories beyond the Standard Model of particle physics. The TUCAN collaboration is constructing a source for ultra-cold neutrons (UCN) and an apparatus to search for the nEDM at TRIUMF, Vancouver, Canada. In this work, we estimate that the spallation-driven UCN source based on a superfluid helium converter will provide (1.38 ± 0.02) 107 polarized UCN at a density of 217 ± 3 UCN/cm3 to a room-temperature EDM experiment per fill. With (1.43 ± 0.02) 106 neutrons detected after the Ramsey cycle, the statistical sensitivity for an nEDM search per storage cycle will be (1.94 ± 0.06) × 10−25 ecm (1σ). The goal sensitivity of 10−27 ecm (1σ) can be reached within 281 ± 16 measurement days.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.012
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.276
Teacher spread0.253 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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