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Simulation of cryogenic buffer gas beams

2021· article· en· W3108774850 on OpenAlexaboutno aff
Yuiki Takahashi, David Shlivko, Gabriel Woolls, Nicholas R. Hutzler

Bibliographic record

VenuePhysical Review Research · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsnot available
FundersNational Institute of Standards and TechnologyMasason FoundationCalifornia Institute of TechnologyGordon and Betty Moore FoundationAlfred P. Sloan FoundationHeising-Simons FoundationNational Science Foundation
KeywordsBuffer gasNozzleRange (aeronautics)Rendering (computer graphics)Computer simulationBeam (structure)MechanicsBuffer (optical fiber)Computer scienceComputational physicsNuclear engineeringPhysicsAerospace engineeringOpticsEngineeringThermodynamicsLaser

Abstract

fetched live from OpenAlex

The cryogenic buffer gas beam (CBGB) is an important tool in the study of cold and ultracold molecules. While there are known techniques to enhance desired beam properties of a CBGB, such as high flux, low velocity, or reduced divergence, they have generally not undergone detailed numerical optimization. Numerical simulation of buffer gas beams is challenging, because the relevant dynamics occur in regions where the density varies by orders of magnitude, rendering typical numerical methods unreliable or intractable. Here we simulate CBGBs with a hybrid approach that combines gas dynamics methods with particle tracing. The simulations capture important properties, such as velocitiy and divergence, across an assortment of designs, including two-stage slowing cells and de Laval nozzles. This approach should therefore be a useful tool for optimizing CBGB designs across a wide range of applications.

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 categoriesInsufficient 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 score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.085
GPT teacher head0.443
Teacher spread0.358 · 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
Published2021
Admission routes1
Has abstractyes

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