Analysis and design of transition radiation in layered uniaxial crystals using Tandem neural networks

TL;DR

This paper introduces a deep learning-based approach to analyze and design transition radiation in layered uniaxial crystals, enabling efficient prediction and inverse design of Cherenkov radiation patterns in nanophotonic structures.

Contribution

It develops a systematic method combining analytical solutions and Tandem neural networks for forward prediction and inverse design of transition radiation in multilayered metamaterials.

Findings

Neural networks accurately predict radiation patterns without electromagnetic simulations.

Tandem neural networks enable inverse design of material properties for desired radiation.

The approach is extendable to other metamaterials like photonic time crystals.

Abstract

With the flourishing development of nanophotonics, Cherenkov radiation pattern can be designed to achieve superior performance in particle detection by fine-tuning the properties of metamaterials such as photonic crystals (PCs) surrounding the swift particle. However, the radiation pattern can be sensitive to the geometry and material properties of PCs, such as periodicity, unit thickness, and dielectric fraction, making direct analysis and inverse design difficult. In this article, we propose a systematic method to analyze and design PC-based transition radiation, which is assisted by deep learning neural networks. By matching boundary conditions at the interfaces, Cherenkov-like radiation of multilayered structures can be resolved analytically using the cascading scattering matrix method, despite the optical axes not being aligned with the swift electron trajectory. Once well trained, forward deep learning neural networks can be utilized to predict the radiation pattern without further direct electromagnetic simulations; moreover, Tandem neural networks have been proposed to inversely design the geometry and/or material properties for desired Cherenkov radiation pattern. Our proposal demonstrates a promising strategy for dealing with layered-medium-based Cherenkov radiation detectors, and it can be extended for other emerging metamaterials, such as photonic time crystals.