Removing grid structure in angle-resolved photoemission spectra via deep learning method

Abstract

Spectroscopic data may often contain unwanted extrinsic signals. For example, in ARPES experiment, a wire mesh is typically placed in front of the CCD to block stray photo-electrons, but could cause a grid-like structure in the spectra during quick measurement mode. In the past, this structure was often removed using the mathematical Fourier filtering method by erasing the periodic structure. However, this method may lead to information loss and vacancies in the spectra because the grid structure is not strictly linearly superimposed. Here, we propose a deep learning method to effectively overcome this problem. Our method takes advantage of the self-correlation information within the spectra themselves and can greatly optimize the quality of the spectra while removing the grid structure and noise simultaneously. It has the potential to be extended to all spectroscopic measurements to eliminate other extrinsic signals and enhance the spectral quality based on the self-correlation of the spectra solely.

References in corpus (6)

• Energy gaps in high-transition temperature cuprate superconductors
• Light-induced emergent phenomena in 2D materials and topological materials
• A precise method for visualizing dispersive features in image plots
• Angle-resolved photoemission spectroscopy and its application to topological materials
• Ultrafast Angle-Resolved Photoemission Spectroscopy of Quantum Materials
• Deep learning-based statistical noise reduction for multidimensional spectral data

Cited by in corpus (2)

• Machine Learning Microscopic Form of Nematic Order in twisted double-bilayer graphene
• Prediction of the Hubbard U parameter from scanning tunneling microscopy images of moire systems using image recognition