連続体の結合状態によって誘発されるファノ共振の線形を予測するための機械学習方法

V S Gerasimov^1,2, A S Kostyukov¹, A E Ershov^1,2

¹International Research Center of Spectroscopy and Quantum Chemistry, Siberian Federal University, Krasnoyarsk, Russia, 660041.

Scientific reports

|August 25, 2025

まとめ

連続体内の対称性保護された結合状態は,介電格子における共振を誘導する. カップルモード理論の式は様々な共鳴形を予測し,機械学習はファノ共鳴線形を正確に予測します.

科学分野:

物理学
光学について
材料科学

背景:

連続体の束縛状態 (BIC) は,エネルギーを放射しない局所的な状態である.
特定の対称性を持つ介電格は,対称性保護BICをホストすることができます.
光学装置の共鳴は光と物質の相互作用を制御するために不可欠です

研究の目的:

介電格子における連続体内の対称性保護結合状態 (BIC) によって誘発される共振を調査する.
共鳴線形を理解し制御するための理論的枠組みを開発する.
共振特性を予測するための機械学習の応用を探求する.

主な方法:

カップルモード理論を用いた一般的な式を導出する.
面内鏡対称性の介電格子の数値シミュレーション
ファノ共振線形を予測するためのランダムフォレスト機械学習の応用

主要な成果:

2 つのパラメータの式は,ファノ,ローレンツ,および反ローレンツプロファイルを含む,伝達力における共振形状を正確に記述します.
トランスミタンスゼロは,上下対称性なくとも一貫して観察されます.
ランダム・フォレスト・マシン・ラーニングは,標準の最小二乗法よりもかなり高い精度でファノ共振線形を予測します.

結論:

結合モード理論は,介電格子におけるBIC誘発共振を分析するための堅固な枠組みを提供します.
伝導率ゼロの存在は一般的な特徴であり,反射率ゼロは対称性近似に依存する.
機械学習は複雑な光学共鳴現象を正確に予測するための強力なツールです

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