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

TL;DR

This paper introduces a deep learning approach to remove grid-like extrinsic signals from ARPES spectra, improving spectral quality without information loss, surpassing traditional Fourier filtering methods.

Contribution

The authors develop a novel deep learning technique that leverages self-correlation within spectra to effectively eliminate grid structures and noise, outperforming conventional filtering.

Findings

Deep learning method effectively removes grid structures from spectra.

The approach preserves spectral information better than Fourier filtering.

Potential to extend to other spectroscopic measurements.

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.