Machine learning method for predicting line-shapes of Fano resonances induced by bound states in the continuum.

V S Gerasimov, A S Kostyukov, A E Ershov, D N Maksimov, V Kimberg, M S Molokeev, S P Polyutov

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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V S GerasimovInternational Research Center of Spectroscopy and Quantum Chemistry, Siberian Federal University, Krasnoyarsk, Russia, 660041.

A S KostyukovInternational Research Center of Spectroscopy and Quantum Chemistry, Siberian Federal University, Krasnoyarsk, Russia, 660041.

A E ErshovInternational Research Center of Spectroscopy and Quantum Chemistry, Siberian Federal University, Krasnoyarsk, Russia, 660041.

D N MaksimovInternational Research Center of Spectroscopy and Quantum Chemistry, Siberian Federal University, Krasnoyarsk, Russia, 660041.

V KimbergTheoretical Chemistry and Biology, KTH Royal Institute of Technology, 106 91, Stockholm, Sweden. kimberg@kth.se.

M S MolokeevKirensky Institute of Physics, Federal Research Center KSC SB RAS, Krasnoyarsk, Russia, 660036.

S P PolyutovInternational Research Center of Spectroscopy and Quantum Chemistry, Siberian Federal University, Krasnoyarsk, Russia, 660041.

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Abstract

We consider resonances induced by symmetry protected bound states in the continuum in dielectric gratings with in-plane mirror symmetry. It is shown that the shape of the resonance in transmittance is controlled by two parameters in a generic formula which can be derived in the framework of the coupled mode theory. It is numerically demonstrated that the formula encompasses various line-shapes including asymmetric Fano, Lorentzian, and anti-Lorentzian resonances. It is confirmed that the transmittance zeros are always present even in the absence up-down symmetry. At the same time reflectance zeros are not generally present in the single mode approximation. It is found that the line-shapes of Fano resonances can be predicted to a good accuracy by the random forest machine learning method which outperforms the standard least square methods approximation in error by an order of magnitude in error with the training dataset size [Formula: see text].

Identifiers

PMID40855166

DOI10.1103/physrevb.72.045138

PMCPMC12379286

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