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

作　　者：Dongchen Huang Junde Liu Tian Qian Yi-Feng Yang 

机构地区：[1]Beijing National Laboratoryfor Condensed Matter Physics,Institute of Physics,Chinese Academy of Sciences,Beijing 100190,China [2]School of Physical Sciences,University of Chinese Academy of Sciences,Beijing 100190,China [3]Songshan Lake Materials Laboratory,Dongguan 523808,China

出　　处：《Science China(Physics,Mechanics & Astronomy)》2023年第6期191-199,共9页中国科学：物理学、力学、天文学（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.11974397,U1832202,and 11888101);the Chinese Academy of Sciences(Grant Nos.QYZDB-SSW-SLH043,XDB33000000;XDB28000000);the Informatization Plan of Chinese Academy of Sciences(Grant No.CAS-WX2021SF-0102);the Synergetic Extreme Condition User Facility(SECUF)。

摘　　要：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 with previous ones,it may well be extended to other fields and application scenarios where obtaining high-quality multidimensional training data is challenging.

关 键 词：DE-NOISING deep learning spectroscopic data 

分 类 号：O433[机械工程—光学工程] TP18[理学—光学]