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

Abstract

Spectroscopic data may often contain unwanted extrinsic signals. For example, in the angle-resolved photoemission spectroscopy (ARPES) experiment, a wire mesh is typically placed in front of the charge-coupled device to block stray photoelectrons but could cause a gridlike 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 overcome this problem effectively. 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.