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.

翻译：光谱数据常常包含不期望的外来信号。例如，在角分辨光电子能谱实验中，通常会在CCD前方放置金属网以阻挡杂散光电子，但在快速测量模式下会导致光谱中出现网格状结构。过去，通常采用数学傅里叶滤波方法通过擦除周期性结构来去除这种结构。然而，由于网格结构并非严格的线性叠加，这种方法可能导致光谱信息丢失和空位。本文提出了一种深度学习方法，有效克服了这一问题。该方法利用光谱自身的自相关信息，在去除网格结构和噪声的同时，能够显著优化光谱质量。该方法有望推广至所有光谱测量领域，仅基于光谱的自相关性，即可消除其他外来信号并提升光谱质量。