Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks.
Journal:
The Review of scientific instruments
Published Date:
Jun 1, 2022
Abstract
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 neural network to demonstrate the prospect of using ML for both denoising of as well as feature extraction from ARPES dispersion maps.