Simulation of caustics caused by high-energy laser reflection from melting metallic targets adapted by a machine learning approach

Abstract

We present a model that calculates the reflected intensity of a high-energy laser irradiating a metallic target. It will enable us to build a laser safety model that can be used to determine nominal ocular hazard distances for high-energy laser engagements. The reflection was first measured in an experiment at 2 m distance from the target. After some irradiation time, the target begins to melt and the reflected intensity presents intensity patterns composed of caustics, which vary rapidly and are difficult to predict. A specific model is developed that produces similar caustic patterns at 2 m distance and can be used to calculate the reflected intensity at arbitrary distances. This model uses a power spectral density (PSD) to describe the melting metal surface. From this PSD, a phase screen is generated and applied onto the electric field of the laser beam, which is then propagated to a distance of 2 m. The simulated intensity distributions are compared to the measured intensity distributions. To quantify the similarity between simulation and experiment, different metrics are investigated. These metrics were chosen by evaluating their correlation with the input parameters of the model. An artificial neural network is then trained, validated and tested on simulated data using the aforementioned metrics to find the input parameters of the PSD that lead to the most similar caustics. Additionally, we tested another approach based on an autoencoder, which was tested on the MNIST dataset, to eventually generate a phase screen directly by using the caustics images.