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

Abstract

Abstract The extraction of high-temperature regions in active regions (ARs) is an important means to help understand the mechanism of coronal heating. The important observational means of high-temperature radiation in ARs is the main emission line of Fe xviii in the 94 Å of the Atmospheric Imaging Assembly. However, 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 predictions. In this paper, we use the outlier detection method to establish the nonlinear correlation between 94 Å and 171, 193, 211 Å based on the former researches by others. A neural network based on 171, 193, 211 Å is constructed to replace the low-temperature emission lines in the ARs of 94 Å. The predicted results are regarded as the low-temperature components of 94 Å, and then the predicted results are subtracted from 94 Å to obtain the outlier component of 94 Å, or Fe xviii. Then, the outlier components obtained by neural network are compared with the Fe xviii obtained 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 differences. In order to analyze the differences between the Fe xviii obtained by the three methods, we subtract the Fe xviii obtained by the DEM and Del’s method from the Fe xviii obtained 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 MK. It is found that there is a great similarity, which also shows that the Fe xviii obtained by DEM and Del’s method still has a large low-temperature component dominated by Fe xiv, and the Fe xviii obtained by neural network is relatively pure.