Prediction of Time-evolving Radial Magnetic Fields on the Solar Surface Using Deep Learning

We develop two artificial intelligence–based models (Models A and B) to predict time-evolving photospheric magnetic fields with an adjustable time step, ranging up to one solar rotation later. Model A predicts future magnetic field data using three consecutive radial magnetic field data sets, each 1...

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Bibliographic Details
Main Authors:	Hyun-Jin Jeong, Stefaan Poedts, Haopeng Wang, Ekatarina Dineva, Panagiotis Gonidakis, Francesco Carella, George Miloshevich, Luis Linan, Harim Lee
Format:	Article
Language:	English
Published:	IOP Publishing 2025-01-01
Series:	The Astrophysical Journal Letters
Subjects:	Solar magnetic fields The Sun Astronomy data analysis Convolutional neural networks Solar magnetic flux emergence
Online Access:	https://doi.org/10.3847/2041-8213/adf5c7
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Prediction of Time-evolving Radial Magnetic Fields on the Solar Surface Using Deep Learning

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