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Record W2051583774 · doi:10.1002/pssc.200460746

A theory of nonlinear susceptibility in polaritonic band‐gap materials doped with multi‐level atoms

2005· article· en· W2051583774 on OpenAlexaff
Debashish Mukherji, Mahi R. Singh

Bibliographic record

VenuePhysica status solidi. C, Conferences and critical reviews/Physica status solidi. C, Current topics in solid state physics · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStrong Light-Matter Interactions
Canadian institutionsWestern University
Fundersnot available
KeywordsPolaritonSuperposition principleCondensed matter physicsPhysicsAtom (system on chip)Nonlinear systemResonance (particle physics)Band gapDopingAtomic physicsQuantum mechanics

Abstract

fetched live from OpenAlex

We have studied the nonlinear susceptibility in dispersive polaritonic band-gap (DPBG) materials in the presence of five-level impurity atoms. Initially, the atom is prepared in a coherent superposition of two levels |b〉 and |c〉. The levels |b〉 and |c〉 are coupled to the upper level |a〉 with a coherent laser field. The nonlinear susceptibility has been calculated corresponding to transitions |a〉 → |c〉 and |a〉 → |b〉 using the equation of motion for the density matrix. The atomic system is interacting with the polariton reservoir. The effect of atom-polariton reservoir coupling on the imaginary part of the nonlinear susceptibility has been studied when the resonance energy lies in lower band of the polariton spectrum. It is found that by shifting the resonance energy away from the band edge, the system goes to the probe gain state. It is also found that by changing the intensity of the coherent laser field, the system switches from gain to loss state and vice-versa. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.079
GPT teacher head0.377
Teacher spread0.298 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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