Haodi Jiang, Jiasheng Wang, Chang Liu, Ju Jing, Hao Liu, Jason T. L. Wang and Haimin Wang
Yasser Abduallah
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 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.
This notebook is Binder enabled and can be run on mybinder.org by using the link below.
Please note that starting Binder might take some time to create and start the image.
For the latest updates of SolarUnet refer to https://github.com/deepsuncode/SolarUnet-magnetic-tracking
Note: Tested on Python version 3.9.13
| Library | Version | Description |
|---|---|---|
| astropy | 5.1 | Astronomy and astrophysics data processing |
| cv2 | 4.7.0 | Image processing |
| keras | 2.11.0 | Artificial neural networks API |
| matplotlib | 3.5.2 | Plotting and graphs |
| numpy | 1.21.5 | Array manipulation |
| scikit-image | 0.19.2 | Image processing |
| scikit-learn | 1.0.2 | Machine learning |
| scipy | 1.9.1 | Science and math |
| tensorflow | 2.11.0 | Neural network libraries |
Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning. H. Jiang, J. Wang, C. Liu, J. Jing, H. Liu, J. T. L. Wang, H. Wang, ApJS, 250:5, 2020.
https://iopscience.iop.org/article/10.3847/1538-4365/aba4aa