Inferring Maps of the Sun's Far-side Unsigned Magnetic Flux from Far-side Helioseismic Images using Machine Learning Techniques
Ruizhu Chen, Junwei Zhao, Shea Hess Webber, Yang Liu, J. Todd, Hoeksema, Marc L. Derosa

TL;DR
This paper develops machine learning models to estimate the Sun's far-side magnetic flux maps from helioseismic images, enabling routine daily monitoring despite observational limitations.
Contribution
The study introduces two machine learning models that convert EUV images and helioseismic data into magnetic-flux maps, improving far-side solar magnetic field estimation.
Findings
Successfully converted EUV images into magnetic-flux maps.
Generated daily far-side magnetic-flux maps over 4.3 years.
Provided a routine method for far-side solar magnetic monitoring.
Abstract
Accurate modeling of the Sun's coronal magnetic field and solar wind structures require inputs of the solar global magnetic field, including both the near and far sides, but the Sun's far-side magnetic field cannot be directly observed. However, the Sun's far-side active regions are routinely monitored by helioseismic imaging methods, which only require continuous near-side observations. It is therefore both feasible and useful to estimate the far-side magnetic-flux maps using the far-side helioseismic images despite their relatively low spatial resolution and large uncertainties. In this work, we train two machine-learning models to achieve this goal. The first machine-learning training pairs simultaneous SDO/HMI-observed magnetic-flux maps and SDO/AIA-observed EUV 304 images, and the resulting model can convert 304 images into magnetic-flux maps. This model is then…
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Taxonomy
TopicsSolar and Space Plasma Dynamics · Solar Radiation and Photovoltaics
