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arXiv · 2203.07537

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

Francisco Restrepo·Junjing Zhao·Utpal Chatterjee
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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 (VAE) 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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Francisco Restrepo, Junjing Zhao, Utpal Chatterjee. 2022-03-14. Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks. https://doi.org/10.1063/5.0090051

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