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Record W4285593911 · doi:10.1103/physreva.106.013513

Quasinormal modes of spheroidal resonators in the null-field framework and application to hybrid anapoles

2022· article· en· W4285593911 on OpenAlexafffund
Benjamin Vennes, Thomas C. Preston

Bibliographic record

VenuePhysical review. A/Physical review, A · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMultipole expansionPhysicsNull (SQL)EigenfunctionExcited stateResonatorQuasinormal modeScatteringInterference (communication)Field (mathematics)Matrix (chemical analysis)Quantum electrodynamicsQuantum mechanicsEigenvalues and eigenvectorsOpticsMaterials science

Abstract

fetched live from OpenAlex

A model for calculating the quasinormal modes (QNMs) of homogeneous and core-shell spheroidal resonators is formulated in the null-field framework. Relying on a multipole expansion of the electromagnetic fields, the Mittag-Leffler theorem is employed to extract the QNMs from the poles of a response matrix. Physical quantities such as the extinction and scattering efficiency and the total internal energy of a mode are expressed directly in terms of the multipole expansion coefficients. We apply the model to study hybrid anapoles, which are radiationless states characterized by an enhancement of the total internal energy. Interference between excited QNMs and the background comprising of all other QNMs leads to a suppression of the extinction efficiency. Further, we show this suppression is due to Fano-like interference between resonant and background multipoles.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.015
GPT teacher head0.353
Teacher spread0.339 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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