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Record W1978155179 · doi:10.1364/oe.22.030559

Enhanced coherence between condensates formed resonantly at different times

2014· article· en· W1978155179 on OpenAlexafffund
Alex Hayat, C. Lange, Lee A. Rozema, Rockson Chang, Shreyas Potnis, H. M. van Driel, Aephraim M. Steinberg, Mark Steger, David W. Snoke, L. N. Pfeiffer, Kenneth West

Bibliographic record

VenueOptics Express · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStrong Light-Matter Interactions
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsCoherence (philosophical gambling strategy)DephasingPhysicsCoherence timePolaritonUltrashort pulseOpticsLaserParticle (ecology)Coherence lengthAtomic physicsCondensed matter physicsQuantum mechanics

Abstract

fetched live from OpenAlex

We demonstrate coherence between exciton-polariton condensates created resonantly at different times. The coherence persists much longer than the individual particle dephasing time, and this persistence increases as the particle density increases. The observed coherence time exceeds that of the injecting laser pulse by more than an order of magnitude. We show that this significant coherence enhancement relies critically on the many-body particle interactions, as verified by its dependence on particle density, interaction strength, and bath temperature, whereas the mass of the particles plays no role in the condensation of resonantly injected polaritons. Furthermore, we observe a large nonlinear phase shift resulting from intra-condensate interaction energy. Our results provide a new approach for probing ultrafast dynamics of resonantly-created condensates and open new directions in the study of coherence in matter.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.261
Teacher spread0.246 · 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

Citations1
Published2014
Admission routes2
Has abstractyes

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