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

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.