Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning

Document Type

Article

Publication Date

9-1-2020

Abstract

Deep learning has drawn significant interest in recent years due to its effectiveness in processing big and complex observational data gathered from diverse instruments. Here we propose a new deep learning method, called SolarUnet, to identify and track solar magnetic flux elements or features in observed vector magnetograms based on the Southwest Automatic Magnetic Identification Suite (SWAMIS). Our method consists of a data preprocessing component that prepares training data from the SWAMIS tool, a deep learning model implemented as a U-shaped convolutional neural network for fast and accurate image segmentation, and a postprocessing component that prepares tracking results. SolarUnet is applied to data from the 1.6 m Goode Solar Telescope at the Big Bear Solar Observatory. When compared to the widely used SWAMIS tool, SolarUnet is faster while agreeing mostly with SWAMIS on feature size and flux distributions and complementing SWAMIS in tracking long-lifetime features. Thus, the proposed physics-guided deep learning-based tool can be considered as an alternative method for solar magnetic tracking.

Identifier

85091393492 (Scopus)

Publication Title

Astrophysical Journal Supplement Series

External Full Text Location

https://doi.org/10.3847/1538-4365/aba4aa

ISSN

00670049

Issue

1

Volume

250

Grant

1821294

Fund Ref

National Science Foundation

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