EGU25-11565, updated on 15 Mar 2025
https://doi.org/10.5194/egusphere-egu25-11565
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Parametrization of SHARP Vector Magnetic Field Using Disentangled Representation Learning
Ekatarina Dineva, George Miloshevich, Giovanni Lapenta, Jasmina Magdalenic Zhukov, and Stefaan Poedts
Ekatarina Dineva et al.
  • KU Leuven, Centre for mathematical Plasma-Astrophysics, Leuven, Belgium (ekaterina.dineva@kuleuven.be)

The rapid growth of high-dimensional data in solar physics presents significant challenges for analysis and interpretation, making it an excellent domain for the application of machine learning (ML) algorithms. Synoptic full-disk observations with the Solar Dynamics Observatory (SDO)  provide continuous observations of the solar magnetic activity over more than one solar cycle, facilitating the study of solar variability and space weather impacts. The Space-weather HMI Active Region Patches (SHARP) vector magnetic field (VMF) maps and parameters, based on Helioseismic and Magnetic Imager (HMI) full-disk observations, are developed to study the magnetic evolution of individual active regions and flare triggering mechanisms. We present a method for active region parametrization by combining empirical parameters and ML-extracted features. Time series of SHARP VMF maps are used as input for the Disentangled Variational Autoencoder (VAE), a Disentangled Representation Learning (DRL) algorithm that facilitates the extraction of a low-dimensional feature representation. The VAE model is used to encode generalized information about nonlinear dynamical systems, i.e., a solar active region, aiming to isolate distinct factors of variation in the data, allowing a clearer interpretation of physical processes. We demonstrate how the ML features can be used to identify and study the stages of the magnetic patches evolution. These are benchmarked with SHARP parameters, relating empirical and learned features. Furthermore, the empirical dataset enhanced with ML features can be used to analyze the development of individual active regions and searching for eruption precursors.

How to cite: Dineva, E., Miloshevich, G., Lapenta, G., Magdalenic Zhukov, J., and Poedts, S.: Parametrization of SHARP Vector Magnetic Field Using Disentangled Representation Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11565, https://doi.org/10.5194/egusphere-egu25-11565, 2025.