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:光谱数据往往含有不需要的外部信号。 例如,在ARPES实验中,电网网网格通常放在CCD前面,以堵塞流散的光电,但在快速测量模式中可能会在光谱中造成类似网格的结构。过去,这种结构往往使用数学四光过滤法,通过去除周期结构而去除。然而,这种方法可能会导致光谱中的信息丢失和空缺,因为网格结构不是纯粹的线性叠加。在这里,我们建议了一种深入的学习方法,以有效克服这一问题。我们的方法利用光谱内部的自我焦热信息,可以大大优化光谱的质量,同时去除网格结构和噪音。它有可能扩大到所有光谱测量方法,以便消除其他外光谱信号,并仅仅根据光谱的自我调节而提高光谱质量。