In recent years, distinct machine learning (ML) models have been separately used for feature extraction and noise reduction from energy-momentum dispersion intensity maps obtained from raw angle-resolved photoemission spectroscopy (ARPES) data. In this work, we employ a shallow variational auto-encoder (VAE) neural network to demonstrate the prospect of using ML for both denoising of as well as feature extraction from ARPES dispersion maps.
Cite
@article{arxiv.2203.07537,
title = {Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks},
author = {Francisco Restrepo and Junjing Zhao and Utpal Chatterjee},
journal= {arXiv preprint arXiv:2203.07537},
year = {2022}
}