Spectroscopic data de-noising via training-set-free deep learning method

• DOI: 10.48550/arxiv.2210.10494
• Publication Date: 2023-04-27
• Authors: Huang, Dongchen; Liu, Junde; Qian, Tian; Yang, Yi-feng
• Keywords: FOS: Computer and information sciences; Condensed Matter - Materials Science; Computer Science - Machine Learning; Physics - Data Analysis, Statistics and Probability; 0103 physical sciences; Materials Science (cond-mat.mtrl-sci); FOS: Physical sciences; 01 natural sciences; Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (cs.LG)

De-noising plays a crucial role in the post-processing of spectra. Machine learning-based methods show good performance in extracting intrinsic information from noisy data, but often require a high-quality training set that is typically inaccessible in real experimental measurements. Here, using spectra in angle-resolved photoemission spectroscopy (ARPES) as an example, we develop a de-noising method for extracting intrinsic spectral information without the need for a training set. This is possible as our method leverages the self-correlation information of the spectra themselves. It preserves the intrinsic energy band features and thus facilitates further analysis and processing. Moreover, since our method is not limited by specific properties of the training set compared to previous ones, it may well be extended to other fields and application scenarios where obtaining high-quality multidimensional training data is challenging.