Extraction and Analysis of Coronal High-temperature Components Based on Outlier Detection

作     者：Li-Yan Sun Kai-Fan Ji Jun-Chao Hong Hui Liu Li-Yan Sun;Kai-Fan Ji;Jun-Chao Hong;Hui Liu

作者机构：Yunnan ObservatoriesChinese Academy of SciencesKunming 650216China University of Chinese Academy of SciencesBeijing 101408China 

出 版 物：《Research in Astronomy and Astrophysics》 (天文和天体物理学研究(英文版))

年 卷 期：2023年第23卷第6期

页      面：143-152页

核心收录：

学科分类：07[理学] 070401[理学-天体物理] 0704[理学-天文学] 

基　　金：supported by the National Natural Science Foundation of China under Grant Nos.U2031140 11873027 and 12073077 

主　　题：Sun:corona Sun:activity methods:statistical methods:data analysis techniques:image processing 

摘      要：The extraction of high-temperature regions in active regions(ARs)is an important means to help understand the mechanism of coronal *** important observational means of high-temperature radiation in ARs is the main emission line of Fe XVⅢin the 94?of the Atmospheric Imaging ***,the diagnostic algorithms for Fe XVⅢ,including the differential emission measure(DEM)and linear diagnostics proposed by Del based on the DEM,have been greatly limited for a long time,and the results obtained are different from the *** this paper,we use the outlier detection method to establish the nonlinear correlation between 94?and 171,193,211?based on the former researches by others.A neural network based on 171,193,211?is constructed to replace the low-temperature emission lines in the ARs of 94?.The predicted results are regarded as the low-temperature components of 94?,and then the predicted results are subtracted from 94?to obtain the outlier component of 94?,or Fe XVⅢ.Then,the outlier components obtained by neural network are compared with the Fe XVⅢobtained by DEM and Del s method,and a high similarity is found,which proves the reliability of neural network to obtain the high-temperature components of ARs,but there are still many *** order to analyze the differences between the Fe XVⅢobtained by the three methods,we subtract the Fe XVⅢobtained by the DEM and Del s method from the Fe XVⅢobtained by the neural network to obtain the residual value,and compare it with the results of Fe XIV in the temperature range of 6.1-6.45 *** is found that there is a great similarity,which also shows that the Fe XVⅢobtained by DEM and Del s method still has a large low-temperature component dominated by Fe XIV,and the Fe XVⅢobtained by neural network is relatively pure.