Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks
This work addresses noise reduction and feature identification in spectroscopy data for materials science researchers, but it appears incremental as it combines existing methods.
The authors tackled the problem of denoising and feature extraction in photoemission spectra by using a shallow variational auto-encoder neural network, demonstrating its potential for both tasks on ARPES dispersion maps.
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.