Line shapes in time- and angle-resolved photoemission spectroscopy explored by machine learning

TL;DR

This paper uses machine learning to analyze time- and angle-resolved photoemission spectroscopy data, revealing how resolution affects line shapes and when exponential decay models are appropriate.

Contribution

It introduces an unsupervised machine learning approach to systematically study TDC line shapes in photoemission data, accounting for resolution effects.

Findings

Finite energy and time resolution influence TDC line shapes.

Machine learning identifies trends in spectral line shapes.

Conditions for exponential decay approximation are clarified.

Abstract

Time- and angle-resolved photoemission spectroscopy is a powerful technique for investigating the dynamics of excited carriers in quantum materials. Typically, data analysis proceeds via the inspection of time distribution curves (TDCs), which represent the time-dependent photoemission intensity in a region of interest -- often chosen somewhat arbitrarily -- in energy-momentum space. Here, we employ $k$-means, an unsupervised machine learning technique, to systematically investigate trends in TDC line shape for quasi-free-standing monolayer graphene and for a simple analytical model. Our analysis reveals how finite energy and time resolution can affect the TDC line shape. We discuss how this can be taken into account in a quantitative analysis, and under what conditions the time-dependent photoemission intensity after laser excitation can be approximated by a simple exponential decay.