Learning the Lantern: Neural network applications to broadband photonic lantern modelling

TL;DR

This paper introduces neural network models that drastically accelerate the simulation of photonic lanterns, enabling rapid and accurate modeling of complex systems with defects and polychromatic data for applications in astronomy and telecommunications.

Contribution

The paper presents a novel neural network approach that achieves over 100,000x speed-up in modeling photonic lanterns, including defect and polychromatic data generalization.

Findings

Neural networks can model photonic lanterns with high accuracy.

Speed-up of over 5 orders of magnitude compared to traditional methods.

Successful application to system optimization and defect modeling.

Abstract

Photonic lanterns allow the decomposition of highly multimodal light into a simplified modal basis such as single-moded and/or few-moded. They are increasingly finding uses in astronomy, optics and telecommunications. Calculating propagation through a photonic lantern using traditional algorithms takes $\sim 1$ hour per simulation on a modern CPU. This paper demonstrates that neural networks can bridge the disparate opto-electronic systems, and when trained can achieve a speed-up of over 5 orders of magnitude. We show that this approach can be used to model photonic lanterns with manufacturing defects as well as successfully generalising to polychromatic data. We demonstrate two uses of these neural network models, propagating seeing through the photonic lantern as well as performing global optimisation for purposes such as photonic lantern funnels and photonic lantern nullers.