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Record W4319313323 · doi:10.1103/physreva.107.023504

Photon-echo-like phenomenon induced by a phonon

2023· article· en· W4319313323 on OpenAlexaff
Yongkang Dong, Chao Pang, Wuliji Hasi, Liang Chen, Xiaoyi Bao, Dengwang Zhou

Bibliographic record

VenuePhysical review. A/Physical review, A · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum optics and atomic interactions
Canadian institutionsUniversity of Ottawa
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsPhysicsPhotonPhononPhotonicsScatteringOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Photon-phonon interaction is a powerful mechanism for all-optical signal processing, slow (fast) light, microscopy, spectroscopy, microwave photonics, and sensing. Here, we demonstrate a photon-echo-like phenomenon induced by a phonon, whose mechanism is represented by a coherent photon-phonon chain interaction (PPCI) theory referring to the alternating evolution between Stokes scattering and anti-Stokes scattering. An analytical solution, including the impulse response of the coherent PPCI theory, is derived to quantitatively analyze the energy conversion between photon and phonon, showing a damped photoacoustic oscillation. All theoretical analysis, numerical simulation, and experiments confirm that the adjacent order echoes have a phase difference of \ensuremath{\pi}, i.e., phase flip, and the echoes generated by using photon and phonon as the initial excitation sources, respectively, are in inverse phase. As a result, the echoes up to the third-order are theoretically analyzed by the proposed coherent PPCI theory and verified by the experiment. This physical mechanism bodes well for a class of photonics applications in telecommunications, optical metrology, and optical computation.

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 score0.004

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 integrity0.0000.001
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 head0.019
GPT teacher head0.369
Teacher spread0.350 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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