论文标题
通过深度学习方法在角度分辨光发射光谱中删除网格结构
Removing grid structure in angle-resolved photoemission spectra via deep learning method
论文作者
论文摘要
光谱数据通常可能包含不必要的外部信号。例如，在ARPES实验中，通常将电线网放在CCD的前面，以阻止流浪光电子，但在快速测量模式下可能会在光谱中引起网格样结构。过去，通常使用数学傅立叶滤波方法通过擦除周期性结构来删除这种结构。但是，此方法可能导致光谱中的信息丢失和空缺，因为网格结构没有严格线性叠加。在这里，我们提出了一种深度学习方法，以有效克服这个问题。我们的方法利用光谱本身内的自相关信息，可以极大地优化光谱的质量，同时消除网格结构和噪声。它有可能扩展到所有光谱测量值，以消除其他外部信号，并基于光谱的自相关增强光谱质量。
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.
