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Stability of quantized vortices in two-component condensates

2023· article· en· W4386887815 on OpenAlexafffund
Sam Patrick, Ansh Gupta, Ruth Gregory, Carlo F. Barenghi

Bibliographic record

VenuePhysical Review Research · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsPerimeter Institute
FundersScience and Technology Facilities CouncilInnovation, Science and Economic Development CanadaInstitut Périmètre de physique théoriqueOntario Ministry of Research, Innovation and ScienceGovernment of CanadaKing's College London
KeywordsVortexPhysicsNucleationBose–Einstein condensateComponent (thermodynamics)TurbulenceCore (optical fiber)Quantum turbulenceCondensed matter physicsClassical mechanicsQuantum mechanicsMechanicsOpticsThermodynamics

Abstract

fetched live from OpenAlex

Multiply quantized vortices (MQVs) within single-component Bose-Einstein condensates are unstable and decay rapidly. We show that MQVs can be stabilized by adding a small number of atoms of a second species to the vortex cores, and that these atoms remain in the vortex core as the system evolves. A consequence of the stabilization is that nearby corotating vortices can orbit in the opposite sense to their individual rotations when enough of the second species is present. This has implications concerning the imaging of vortices, as well as quantum turbulence and vortex nucleation in two-component condensates, such as those involving mixtures of $^{87}\mathrm{Rb}$ and $^{133}\mathrm{Cs}$.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 studies0.0010.001
Scholarly communication0.0010.001
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 head0.151
GPT teacher head0.486
Teacher spread0.335 · 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 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

Citations14
Published2023
Admission routes2
Has abstractyes

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