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Annals of Data Science

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02.08.2022

High Resolution Solar Image Generation Using Generative Adversarial Networks

verfasst von: Ankan Dash, Junyi Ye, Guiling Wang, Huiran Jin

Erschienen in: Annals of Data Science | Ausgabe 5/2024

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Abstract

Der Artikel geht der Bedeutung von Sonnenphänomenen wie Sonneneruptionen und koronalen Massenauswürfen für die Vorhersage des Weltraumwetters nach. Er diskutiert die Rolle von NASA-Missionen wie SDO bei der Erfassung von Sonnenbeobachtungen und die Herausforderung, Sonnenereignisse aufgrund von Datenlücken vorherzusagen. Die Studie stellt den Einsatz von Generative Adversarial Networks (GANs) vor, insbesondere des Pix2PixHD-Modells, um hochauflösende SDO / AIA0304-Bilder aus SDO / HMI-Magnetogrammen zu erzeugen. Die Autoren modifizieren und vergleichen die Pix2Pix- und Pix2PixHD-Algorithmen und zeigen die überlegene Leistung von Pix2PixHD bei der Erfassung komplexer Merkmale und feiner Details. Die Ergebnisse deuten darauf hin, dass GANs die Vorhersagefähigkeiten von Sonnenereignissen deutlich verbessern können, was den Artikel zu einer fesselnden Lektüre für diejenigen macht, die sich für die Schnittmenge von Solarphysik und fortgeschrittenen maschinellen Lerntechniken interessieren.

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Abstract

We applied Deep Learning algorithm known as Generative Adversarial Networks (GANs) to perform solar image-to-image translation. That is, from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) line of sight magnetogram images to SDO/Atmospheric Imaging Assembly (AIA) 0304-Å images. The Ultraviolet (UV)/Extreme Ultraviolet observations like the SDO/AIA 0304-Å images were only made available to scientists in the late 1990s even though the magnetic field observations like the SDO/HMI have been available since the 1970s. Therefore, by leveraging Deep Learning algorithms like GANs we can give scientists access to complete datasets for analysis. For generating high resolution solar images, we use the Pix2PixHD and Pix2Pix algorithms. The Pix2PixHD algorithm was specifically designed for high resolution image generation tasks, and the Pix2Pix algorithm is by far the most widely used image to image translation algorithm. For training and testing we used the data for the year 2012, 2013 and 2014. After model training, we evaluated the model on the test data. The results show that our deep learning models are capable of generating high resolution (1024 × 1024 pixels) SDO/AIA0304 images from SDO/HMI line of sight magnetograms. Specifically, the pixel-to-pixel Pearson Correlation Coefficient of the images generated by Pix2PixHD and original images is as high as 0.99. The number is 0.962 if Pix2Pix is used to generate images. The results we get for our Pix2PixHD model is better than the results obtained by previous works done by others to generate SDO/AIA 0304 images. Thus, we can use these models to generate AIA0304 images when the AIA0304 data is not available which can be used for understanding space weather and giving researchers the capability to predict solar events such as Solar Flares and Coronal Mass Ejections. As far as we know, our work is the first attempt to leverage Pix2PixHD algorithm for SDO/HMI to SDO/AIA0304 image-to-image translation.

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Titel: High Resolution Solar Image Generation Using Generative Adversarial Networks
verfasst von: Ankan Dash
Junyi Ye
Guiling Wang
Huiran Jin
Publikationsdatum: 02.08.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Annals of Data Science / Ausgabe 5/2024
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-022-00436-2

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High Resolution Solar Image Generation Using Generative Adversarial Networks

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