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Record W4322580680 · doi:10.4236/jamp.2023.112036

Polarization Simultons in CARS by Polaritons

2023· article· en· W4322580680 on OpenAlexaff
Vladimir Feshchenko, Galina Feshchenko

Bibliographic record

VenueJournal of Applied Mathematics and Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNonlinear Photonic Systems
Canadian institutionsVanier CollegeDawson College
Fundersnot available
KeywordsPhysicsPolarization (electrochemistry)PolaritonStokes parametersAnisotropyRaman scatteringLinear polarizationOpticsPerpendicularScatteringDipoleCondensed matter physicsRaman spectroscopyQuantum mechanicsChemistryLaserGeometry

Abstract

fetched live from OpenAlex

The system of shortened Maxwell’s equations simulating the processes of evolution of the stimulated Raman scattering (SRS) by polaritons in anisotropic dipole-active crystals is obtained. The theory was developed for the case of cubic crystals which become anisotropic due to the deformation of the dielectric constant by the linearly polarized pump wave. The pump field is a linearly polarized plane electromagnetic wave. We report the results of the theoretical investigation of the possibility of the existence of a regime of pulse propagation as simultaneous travel of solitary waves in coherent anti-Stokes stimulated Raman scattering by polaritons in anisotropic crystals. The emphasis was made on the existence of both Stokes and anti-Stokes pulses propagating with two stable and perpendicular to the direction of travel polarizations. We showed the theoretical possibility of simultaneous propagation of pulses not only at frequencies of Stokes and anti-Stokes waves but the pump frequency as well. We obtained the expression for the gain factor g. It is also shown that the expression for g is consistent with the experimental results for the spectra of ZnS.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.248
Teacher spread0.238 · 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 designSimulation or modeling
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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