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Record W1986173507 · doi:10.1103/physreva.80.022712

Observation of quantum diffractive collisions using shallow atomic traps

2009· article· en· W1986173507 on OpenAlexafffund
David E. Fagnan, Jicheng Wang, Chenchong Zhu, Pavle Djuricanin, B.G. Klappauf, James L. Booth, Kirk W. Madison

Bibliographic record

VenuePhysical Review A · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsBritish Columbia Institute of TechnologyUniversity of British Columbia
FundersBritish Columbia Institute of TechnologyNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsTrap (plumbing)Magnetic trapAtomic physicsTrappingMagneto-optical trapPhysicsCross section (physics)QuantumPenning trapCollisionLaserElectronNuclear physicsOpticsPlasmaQuantum mechanics

Abstract

fetched live from OpenAlex

We present measurements and calculations of the trap loss rate for laser-cooled $^{87}\text{R}\text{b}$ atoms confined in either a magneto-optic trap (MOT) or a magnetic quadrupole trap when exposed to a room-temperature background gas of Ar. We study the loss rate as a function of trap depth and find that copious glancing elastic collisions, which occur in the so-called quantum diffractive regime and impart very little energy to the trapped atoms, result in significant differences in the loss rate for the MOT compared to a pure magnetic trap due solely to the difference in potential depth. This finding highlights the importance of knowing the trap depth when attempting to infer the total collision cross section from measurements of trap loss rates. Moreover, this variation in trap loss rate with trap depth can be used to extract information about the differential cross section.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.040
GPT teacher head0.331
Teacher spread0.291 · 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 designObservational
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

Citations49
Published2009
Admission routes2
Has abstractyes

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