Model-free detection of physical order from scattering and imaging data using escort-weighted Shannon entropy and divergence matrices

Abstract

We demonstrate a model-free data analysis framework that leverages escort-weighted Shannon entropy and several divergence matrices to detect phase transitions in scattering and imaging datasets. By establishing a connection between physical entropy and informational entropy, this approach provides a sensitive method for identifying phase transitions without a physical model or order parameter. We further show that pairwise divergence matrices, in particular, Kullback-Leibler divergence, Jeffrey divergence, Jensen-Shannon divergence, and difference Kullback-Leibler divergence, provide more comprehensive measures of statistical changes than scalar entropy alone. Our approach successfully detects the onset of both long- and short-range order in neutron and x-ray scattering data, as well as a nontrivial phase transition in magnetic skyrmion lattices observed through Lorentz-transition electron microscopy. These results establish a framework for fast, automated, model-free detection of physical order in experimental data with broad applications in materials science and condensed matter physics.