mlsanityData to 14 Oct 2026

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

arXiv 2203.07537 ↗First posted 14 Mar 2022physics.ins-det cond-mat.str-el cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 13 references that are in the map. First posted on arXiv 14 Mar 2022.

Builds on

3 of 13 references.

References · 3

  1. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
  2. 2021Deep learning-based statistical noise reduction for multidimensional spectral datacited by 9
  3. 2020Super Resolution Convolutional Neural Network for Feature Extraction in Spectroscopic Datacited by 7

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2025A simulation-based training framework for machine-learning applications in ARPEScited by 1
  2. 2024Training-set-free two-stage deep learning for spectroscopic data de-noisingcited by 0