Machine Learning to Analyze Images of Shocked Materials for Precise and Accurate Measurements

TL;DR

This paper introduces LADA, a supervised machine learning algorithm for precise boundary detection in shock physics images, providing statistically rigorous analysis of features like crystals and shock-induced density changes.

Contribution

The paper presents LADA, a novel image segmentation method that adaptively locates features in shock images without relying on predefined models, enhancing analysis accuracy.

Findings

LADA accurately locates shock wave features with statistical uncertainties.

The algorithm effectively analyzes diverse physical phenomena in shock images.

LADA operates independently of specific physical models or simulations.

Abstract

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast images of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations