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Measuring solar magnetic fields with artificial neural networks
1High Altitude Observatory, NCAR, Boulder CO 80307-3000, USA. navarro@ucar.edu
Summary
Scientists developed a new AI method to quickly measure the Sun's magnetic field. This machine learning approach analyzes solar observations, enabling faster and more detailed studies of solar activity.
Area of Science:
- Solar physics
- Heliophysics
- Astrophysics
Background:
- Quantifying the solar magnetic field is essential for understanding solar dynamics and activity.
- Current methods for inferring solar magnetic fields are computationally intensive, limiting analysis to small regions and low resolution.
Purpose of the Study:
- To develop a computationally efficient method for inferring solar magnetic fields.
- To enable routine analysis of large-scale, high-resolution solar observations.
Main Methods:
- A multilayer perceptron (a type of neural network) was trained using synthetic solar magnetic field profiles.
- The trained network was used to recognize magnetic field profiles from real sunspot observations.
- The network's results were compared quantitatively with traditional inversion techniques.
Main Results:
- The multilayer perceptron accurately recognized synthetic profiles and inferred magnetic fields.
- The AI method demonstrated reliability for magnetic filling factors exceeding approximately 70% in sunspot observations.
- A significant reduction in computation time was achieved compared to traditional methods.
Conclusions:
- The developed AI approach offers a reliable and computationally efficient alternative for solar magnetic field quantification.
- This method has the potential to revolutionize the analysis of large-scale, high-resolution solar magnetic field data.
- Enables faster and more comprehensive studies of solar activity and variability.