# Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning

arXiv: https://arxiv.org/abs/2008.12080 · HTML: https://mlsanity.com/paper/2008.12080/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [Machine learning in solar physics](https://mlsanity.com/paper/2306.15308/) (2023)
- [Surya: Foundation Model for Heliophysics](https://mlsanity.com/paper/2508.14112/) (2025)
- [Tracing Halpha Fibrils through Bayesian Deep Learning](https://mlsanity.com/paper/2107.07886/) (2021)
- [A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data](https://mlsanity.com/paper/2211.02278/) (2022)
- [AI Foundation Model for Heliophysics: Applications, Design, and Implementation](https://mlsanity.com/paper/2410.10841/) (2024)
- [Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network](https://mlsanity.com/paper/2403.18302/) (2024)
- [Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges](https://mlsanity.com/paper/2409.04850/) (2024)
- [Deep Learning Based Reconstruction of Total Solar Irradiance](https://mlsanity.com/paper/2107.11042/) (2021)
- [Efficient labeling of solar flux evolution videos by a deep learning model](https://mlsanity.com/paper/2308.14976/) (2023)

## Abstract (arXiv)

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.