  Deep learning has drawn a lot of 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 pre-processing
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 post-processing component that prepares
tracking results. SolarUnet is applied to data from the 1.6 meter 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.
