MétaCan
Menu
Back to cohort
Record W2805103908 · doi:10.1103/physreva.98.023611

Atom-optics knife edge: Measuring narrow momentum distributions

2018· article· en· W2805103908 on OpenAlexafffund
Ramón Ramos, David Spierings, Shreyas Potnis, Aephraim M. Steinberg

Bibliographic record

VenuePhysical review. A/Physical review, A · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of Toronto
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y TecnologíaCanadian Institute for Advanced ResearchJohn E. Fetzer Memorial Trust
KeywordsMomentum (technical analysis)PhysicsEnhanced Data Rates for GSM EvolutionAtom (system on chip)InterferometryOpticsUltracold atomMeasure (data warehouse)Atomic physicsAngular momentumAtom interferometerQuantum tunnellingComputational physicsResolution (logic)Astronomical interferometerCondensed matter physicsQuantum mechanics

Abstract

fetched live from OpenAlex

By employing the equivalent of a knife-edge measurement for matter waves, we are able to characterize ultralow-momentum widths. We measure a momentum width corresponding to an effective temperature of $0.9\phantom{\rule{0.28em}{0ex}}\ifmmode\pm\else\textpm\fi{}\phantom{\rule{0.28em}{0ex}}0.2$ nK, limited only by our cooling performance. To achieve similar resolution using standard methods would require hundreds of milliseconds of expansion or Bragg beams with tens of Hz frequency stability. Furthermore, we show evidence of tunneling in a one-dimensional system when the ``knife-edge'' barrier is spatially thin. This method is a useful tool for atomic interferometry and for other areas in cold-atom physics where a robust and precise technique for characterizing the momentum distribution is crucial.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.003

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 head0.340
Teacher spread0.317 · 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.

Study designTheoretical or conceptual
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

Citations13
Published2018
Admission routes2
Has abstractyes

Explore more

Same venuePhysical review. A/Physical review, ASame topicCold Atom Physics and Bose-Einstein CondensatesFrench-language works237,207