Role of photonic interference in exciton-mediated magneto-optic responses

TL;DR

This paper investigates how photonic interference influences exciton-magnon interactions in van der Waals magnets, revealing complex, non-linear optical responses and proposing machine learning for optimizing magnon-photon transduction.

Contribution

It uniquely disentangles exciton-magnon coupling from magnon-photon interactions and highlights the role of photonic effects in shaping optical responses.

Findings

Photonic interference significantly affects exciton-magnon interactions.

Thermal magnons can alter optical signatures qualitatively.

Machine learning can optimize magnon-photon transduction.

Abstract

Coupled optical and magnetic excitations can give rise to remarkably strong magneto-optic responses. This is particularly evident in van der Waals magnets, such as the antiferromagnet CrSBr, where excitons and magnons emerge from the same electronic orbitals. While previous work has primarily focused on uncovering the magneto-electric origin of the resulting exciton-magnon interactions, the influence of photonic effects has received comparatively little attention. Here, we use numerical simulations to disentangle exciton-magnon coupling from the exciton-mediated magnon-photon interactions observed in optical experiments. Our simulations show the strong dependence of these interactions on photonic interference and dispersion effects near excitonic resonances. Such effects shape the optical response to coherent magnons and make it intrinsically non-linear in the magnon-induced exciton energy shift. Thermal magnons, which have a particularly pronounced impact on excitons, are found to even produce qualitatively different trends in optical signatures. Depending on weak or strong coupling of excitons and photons, the same exciton-magnon interaction can lead to a red-shift of optical modes, a nearly vanishing response, or their blue-shift. Finally, we demonstrate first steps towards optimizing the multi-parameter problem of efficient magnon-photon transduction using a machine-learning approach.