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Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks
Francisco Restrepo1, Junjing Zhao1, Utpal Chatterjee1
1Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA.
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.
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