Electron dynamics in a three-dimensional Brillouin zone analysed by machine learning

TL;DR

This paper demonstrates the use of unsupervised machine learning, specifically k-means clustering, to analyze complex four-dimensional TR-ARPES data of Weyl semimetal PtBi₂, revealing subtle electron dynamics variations.

Contribution

It introduces a novel application of machine learning to interpret high-dimensional electron dynamics data in topological materials.

Findings

k-means effectively identifies trends in 4D data

Reveals subtle variations near Weyl points

Enhances analysis of electron dynamics in complex materials

Abstract

The electron dynamics in the unoccupied states of the Weyl semimetal PtBi$_2$ is studied by time- and angle-resolved photoemission spectroscopy (TR-ARPES). The measurement's result is the photoemission intensity $I$ as a function of at least four parameters: the emission angle and kinetic energy of the photoelectrons, the time delay between pump and probe laser pulses, and the probe laser photon energy that needs to be varied to access the full three-dimensional Brillouin zone of the material. The TR-ARPES results are reported in an accompanying paper. Here we focus on the technique of using $k$-means, an unsupervised machine learning technique, in order to discover trends in the four-dimensional data sets. We study how to compare the electron dynamics across the entire data set and how to reveal subtle variations between different data sets collected in the vicinity of the bulk Weyl points.