Speaker
Description
First Name: Junmu
Last Name: Youn
Affiliation: Kyung Hee University
All Authors: Junmu Youn, Jeongwoo Lee , Mingyu Jeon, Daeil Kim, Seungwoo Ahn, Youngjae Kim, Hyun-Jin Jeong, and Yong-Jae Moon
Abstract: In this work, we introduce a method for determining the differential emission measures (DEMs) using Solar Orbiter/Extreme Ultraviolet Imager (EUI)/Full Sun Imager (FSI) and AI-generated EUV data. The FSI captures only two full-disk extreme UV (EUV) channels (174 and 304 Å), which imposes constraints on determining DEMs. We address this problem using deep learning models based on Pix2PixCC, trained using the Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) dataset. These models successfully generate five-channel (94, 131, 193, 211, and 335 Å) EUV data from 171 and 304 Å EUV observations with high correlation coefficients. We then apply the trained models to the Solar Orbiter/EUI/FSI dataset and generate the five-channel data that the FSI cannot observe. Here, we use the regularized inversion method to compare the DEMs derived from the SDO/AIA dataset with those from the AI-augmented FSI dataset, demonstrating that the AI-generated results are consistent with the ground truth. With this methodology, we track AR13664 over three solar rotations. During this period, Solar Orbiter was located almost behind the Sun. Thus, we could continuously examine DEM variations of this active region. This allows us to follow the DEM evolution of the active region even when it has rotated behind the limb from AIA’s viewpoint. Consequently, our method enables a more complete and extended analysis of the coronal structures associated with the AR than would be possible using AIA observations alone.