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

arXiv: https://arxiv.org/abs/2203.07537 · HTML: https://mlsanity.com/paper/2203.07537/

## 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 (most cited first, 3 of 3)

- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Deep learning-based statistical noise reduction for multidimensional spectral data](https://mlsanity.com/paper/2107.00844/) (2021)
- [Super Resolution Convolutional Neural Network for Feature Extraction in Spectroscopic Data](https://mlsanity.com/paper/2001.10908/) (2020)

## Built on by (most cited first, 2 of 2)

- [A simulation-based training framework for machine-learning applications in ARPES](https://mlsanity.com/paper/2508.15983/) (2025)
- [Training-set-free two-stage deep learning for spectroscopic data de-noising](https://mlsanity.com/paper/2402.18830/) (2024)

## 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.