Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks

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-enco...

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Bibliographic Details
Published in:arXiv.org 2022-03
Main Authors: Restrepo, Francisco, Zhao, Junjing, Chatterjee, Utpal
Format: Article
Language:English
Subjects:
Coders
Dispersion
Feature extraction
Machine learning
Neural networks
Noise reduction
Photoelectric emission
Spectrum analysis
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2203.07537