  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.
