Optimization and realignment of OAM mode excitation in ring-core optical fibers using machine learning.

Light beams carrying orbital angular momentum (OAM) in free space or within optical fibers have a wide range of applications in optics; however, exciting these modes with both high purity and low loss generally requires demanding optimization of excitation conditions in a high dimensional space. Furthermore, mechanical drift can significantly degrade the mode purity over time, which may limit practical deployment of OAM modes in concrete applications. Here, combining an iterative wavefront matching approach and a genetic algorithm, we demonstrate rapid and automated excitation of OAM modes with optimized purity and reduced loss. Our approach allows for systematic computational realignment of the system enabling drift compensation over extended durations. Our experimental results indicate that OAM purity can be optimized and maintained over periods exceeding 24 h, paving the way for the applications of stable OAM beams in optics.