Theory of noise in high-gain surface plasmon-polariton amplifiers incorporating dipolar gain media
Bibliographic record
Abstract
A theoretical analysis of noise in high-gain surface plasmon-polariton amplifiers incorporating dipolar gain media is presented. An expression for the noise figure is obtained in terms of the spontaneous emission rate into the amplified surface plasmon-polariton taking into account the different energy decay channels experienced by dipoles in close proximity to the metallic surface. Two amplifier structures are examined: a single-interface between a metal and a gain medium and a thin metal film bounded by identical gain media on both sides. A realistic configuration is considered where the surface plasmon-polariton undergoing amplification has a Gaussian field profile in the plane of the metal and paraxial propagation along the amplifier's length. The noise figure of these plasmonic amplifiers is studied considering three prototypical gain media with different permittivities. It is shown that the noise figure exhibits a strong dependance on the real part of the permittivities of the metal and gain medium, and that its minimum value is 4/π(∼3.53 dB). The origin of this minimum value is discussed. It is also shown that amplifier configurations supporting strongly confined surface plasmon-polaritons suffer from a large noise figure, which follows from an enhanced spontaneous emission rate due to the Purcell effect.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".