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Tuning the Dipole-Dipole Interaction in a Quantum Gas with a Rotating Magnetic Field

2018· article· en· W2794641069 on OpenAlexfundno aff
Yijun Tang, Wil Kao, Kuan-Yu Li, Benjamin Lev

Bibliographic record

VenuePhysical Review Letters · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsnot available
FundersAir Force Office of Scientific ResearchNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsPhysicsDipoleMagnetic fieldCondensed matter physicsVortexQuantumMagnetic dipole–dipole interactionRotation (mathematics)Magnetic dipoleLattice (music)Bose–Einstein condensateQuantum mechanicsMechanics

Abstract

fetched live from OpenAlex

We demonstrate the tuning of the magnetic dipole-dipole interaction (DDI) within a dysprosium Bose-Einstein condensate by rapidly rotating the orientation of the atomic dipoles. The tunability of the dipolar mean-field energy manifests as a modified gas aspect ratio after time-of-flight expansion. We demonstrate that both the magnitude and the sign of the DDI can be tuned using this technique. In particular, we show that a magic rotation angle exists at which the mean-field DDI can be eliminated, and at this angle, we observe that the expansion dynamics of the condensate is close to that predicted for a nondipolar gas. The ability to tune the strength of the DDI opens new avenues toward the creation of exotic soliton and vortex states as well as unusual quantum lattice phases and Weyl superfluids.

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.013
GPT teacher head0.280
Teacher spread0.267 · 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

Citations73
Published2018
Admission routes1
Has abstractyes

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