A simulation-based training framework for machine-learning applications in ARPES

TL;DR

This paper introduces aurelia, an open-source simulator for generating synthetic ARPES spectra to train machine learning models, demonstrating improved spectra quality assessment over human analysis and enabling efficient experimental workflows.

Contribution

The paper presents a novel synthetic ARPES spectra generator and demonstrates its effectiveness in training ML models for spectra quality evaluation, addressing data scarcity issues.

Findings

ML model trained on synthetic data outperforms human analysis

Synthetic spectra can effectively replace experimental data for training

Model accurately identifies optimal measurement regions

Abstract

In recent years, angle-resolved photoemission spectroscopy (ARPES) has advanced significantly in its ability to probe more observables and simultaneously generate multi-dimensional datasets. These advances present new challenges in data acquisition, processing, and analysis. Machine learning (ML) models can drastically reduce the workload of experimentalists; however, the lack of training data for ML -- and in particular deep learning -- is a significant obstacle. In this work, we introduce an open-source synthetic ARPES spectra simulator - aurelia - for the purpose of generating the large datasets necessary to train ML models. As a demonstration, we train a convolutional neural network to evaluate ARPES spectra quality -- a critical task performed during the initial sample alignment phase of the experiment. We benchmark the simulation-trained model against actual experimental data and find that it can assess the spectra quality more accurately than human analysis, and swiftly identify the optimal measurement region with high precision. Thus, we establish that simulated ARPES spectra can be an effective proxy for experimental spectra in training ML models.