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Record W1977548113 · doi:10.1103/physreva.79.013412

Laser cooling with a modified optical shaker

2009· article· en· W1977548113 on OpenAlexaff
L. Marmet

Bibliographic record

VenuePhysical Review A · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsShakerLaser coolingPhysicsLaserWater coolingWork (physics)Resolved sideband coolingOpticsPhase (matter)Power (physics)MechanicsAtomic physicsAcousticsThermodynamicsVibrationQuantum mechanics

Abstract

fetched live from OpenAlex

Some practical improvements are proposed for the ``optical-shaker'' laser-cooling technique [I. S. Averbukh and Y. Prior, Phys. Rev. Lett. 94, 153002 (2005)]. The improved technique results in an increased cooling rate and decreases the minimum cooling temperature achievable with the optical shaker. The modified shaker requires only one measurement of the force on the atoms before each cooling step, resulting in a simplification of the feedback electronics. The force is inferred from the power variations of the transmitted laser beams and is used to determine the best moment at which the cooling steps are applied. The temperature of the atomic system is automatically monitored, which allows maintaining an optimum cooling rate as the temperature decreases. The improved shaker is simple to build, provides a faster rate of cooling, and can work in the microkelvin regime. Numerical modeling shows a reduction by a factor of 3 in the required number of phase jumps and a lower temperature limit reduced by an order of magnitude compared to the initially proposed shaker. The technique is also extendable to cooling in three dimensions.

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)0.001
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.283
Teacher spread0.269 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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