Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks
- Resource Type
- Authors
- Francisco Restrepo; Junjing Zhao; Utpal Chatterjee
- Source
- Subject
- Machine Learning
FOS: Computer and information sciences
Condensed Matter - Strongly Correlated Electrons
Computer Science - Machine Learning
Physics - Instrumentation and Detectors
Strongly Correlated Electrons (cond-mat.str-el)
Condensed Matter::Superconductivity
FOS: Physical sciences
Neural Networks, Computer
Instrumentation and Detectors (physics.ins-det)
Instrumentation
Machine Learning (cs.LG)
- Language
- English
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.
Submitted to Review of Scientific Instruments