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Determination of Linear Force-Free Magnetic Field Constant Alpha Using Deep Learning View all abstracts by submitter

Bernard Benson   Submitted: 2017-12-04 17:12

Modeling the coronal magnetic field of the Sun is an important objective in heliophysics. In this study, we illustrate how to use deep learning to estimate the parameter for a magnetic field model. A linear force-free magnetic field configuration is employed to model, as an initial illustration, an active region by using two magnetic dipoles and to determine the associated linear force-free field (LFFF) α parameter from a set of pseudocoronal loop images which serve as training and validation sets to existing deep learning algorithms. Our results show very high accuracy of determining the LFFF parameter α from pseudocoronal loop images. Keywords?Sun; solar corona; solar activity; magnetic field models; deep learning;

Authors: Bernard Benson, Zhuocheng Jiang, W. David Pan, G. Allen Gary and Qiang Hu
Projects: SDO-AIA

Publication Status: Accepted to CSCI-ISAI 2017, Las Vegas Conference
Last Modified: 2017-12-05 11:51
Go to main E-Print page  Two-scale analysis of solar magnetic helicity   	Heating and cooling of coronal loops with turbulent suppression of parallel heat conduction  Edit Entry  Download Preprint  Delete Entry 

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