Phase identification despite amplitude variation in a coherent beam combination using deep learning

Abstract

Coherent beam combination offers the potential for surpassing the power limit of a single fibre laser, as well as achieving agile far-field beam-shaping. However, the spatial beam profile of the combined beam is significantly dependent on the phase of each fibre. Recent results have shown that deep learning can be used to extract phase information from a far-field intensity profile, hence unlocking the potential for real-time control. However, the far-field intensity profile is also dependent on the amplitude of each fibre, and therefore phase identification may also need to occur whilst the fibre amplitudes are not equal. Here, it is shown that a neural network trained to identify phase when all fibres have equal amplitudes can also identify phase values when the amplitudes are not equal, without requiring additional training data.