Shin, Gyungin and Moon, Yong-Jae and Park, Eunsu and Jeong, Hyun-Jin and Lee, Harim and Bae, Sung-Ho (2020) Generation of High-resolution Solar Pseudo-magnetograms from Ca ii K Images by Deep Learning. The Astrophysical Journal, 895 (1). L16. ISSN 2041-8213
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Abstract
In this Letter, we generate realistic high-resolution (1024 × 1024 pixels) pseudo-magnetograms from Ca ii K images using a deep learning model based on conditional generative adversarial networks. For this, we consider a model "pix2pixHD" that is specifically devised for high-resolution image translation tasks. We use Ca ii K 393.3 nm images from the Precision Solar Photometric Telescope at the Rome Observatory and line-of-sight magnetograms from the Helioseismic and Magnetic Imager (HMI) at the Solar Dynamics Observatory from 2011 January to 2015 June. 2465 pairs of Ca ii K and HMI are used for training except for January and July data. The remaining 436 pairs are used for an evaluation of the model. Our model shows that the mean correlation coefficient (CC) of total unsigned magnetic flux between AI-generated and real ones is 0.99 and the mean pixel-to-pixel CC after 8 × 8 binning over the full disk is 0.74. We find that the AI-generated absolute magnetic flux densities are highly consistent with real ones, even to the fine scale structures of quiet regions. On the other hand, the mean pixel-to-pixel correlations of magnetic flux densities strongly depend on a region of interest: 0.81 for active regions and 0.24 for quiet regions. Our results suggest a sufficient possibility that we can produce high-resolution solar magnetograms from historical Ca ii data.
Item Type: | Article |
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Subjects: | OA Open Library > Physics and Astronomy |
Depositing User: | Unnamed user with email support@oaopenlibrary.com |
Date Deposited: | 27 May 2023 05:35 |
Last Modified: | 24 Jan 2024 04:00 |
URI: | http://archive.sdpublishers.com/id/eprint/858 |