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Record W2274916723 · doi:10.1088/1367-2630/18/7/073028

Precision measurement of the nuclear polarization in laser-cooled, optically pumped<sup>37</sup>K

2016· article· en· W2274916723 on OpenAlexafffund

Bibliographic record

VenueNew Journal of Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsUniversity of ManitobaTRIUMF
FundersIsrael Science FoundationNatural Sciences and Engineering Research Council of CanadaNational Research CentreU.S. Department of Energy
KeywordsPolarization (electrochemistry)PopulationAtom (system on chip)Degree of polarizationMeasure (data warehouse)Systematic error

Abstract

fetched live from OpenAlex

We report a measurement of the nuclear polarization of laser-cooled, optically-pumped $^{37}\mathrm{K}$ atoms which will allow us to precisely measure angular correlation parameters in the beta-decay of the same atoms. These results will be used to test the $V-A$ framework of the weak interaction at high precision. At the TRIUMF Neutral Atom Trap (TRINAT), a magneto-optical trap (MOT) confines and cools neutral $^{37}\mathrm{K}$ atoms and optical pumping spin-polarizes them. We monitor the nuclear polarization of the same atoms that are decaying in situ by photoionizing a small fraction of the partially polarized atoms and then use the standard optical Bloch equations to model their population distribution. We obtain an average nuclear polarization of $P = 0.9913\pm0.0008$, which is significantly more precise than previous measurements with this technique. Since our current measurement of the beta-asymmetry has $0.2\%$ statistical uncertainty, the polarization measurement reported here will not limit its overall uncertainty. This result also demonstrates the capability to measure the polarization to $<0.1\%$, allowing for a measurement of angular correlation parameters to this level of precision, which would be competitive in searches for 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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.363

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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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

Citations12
Published2016
Admission routes2
Has abstractyes

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