Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network

Abstract

We propose a new machine learning approach to Stokes inversion based on a convolutional neural network (CNN) and the Milne-Eddington (ME) method. The Stokes measurements used in this study were taken by the Near InfraRed Imaging Spectropolarimeter (NIRIS) on the 1.6 m Goode Solar Telescope (GST) at the Big Bear Solar Observatory. By learning the latent patterns in the training data prepared by the physics-based ME tool, the proposed CNN method is able to infer vector magnetic fields from the Stokes profiles of GST/NIRIS. Experimental results show that our CNN method produces smoother and cleaner magnetic maps than the widely used ME method. Furthermore, the CNN method is 4~6 times faster than the ME method, and is able to produce vector magnetic fields in near real-time, which is essential to space weather forecasting. Specifically, it takes ~50 seconds for the CNN method to process an image of 720 x 720 pixels comprising Stokes profiles of GST/NIRIS. Finally, the CNN-inferred results are highly correlated to the ME-calculated results and are closer to the ME's results with the Pearson product-moment correlation coefficient (PPMCC) being closer to 1 on average than those from other machine learning algorithms such as multiple support vector regression and multilayer perceptrons (MLP). In particular, the CNN method outperforms the current best machine learning method (MLP) by 2.6% on average in PPMCC according to our experimental study. Thus, the proposed physics-assisted deep learning-based CNN tool can be considered as an alternative, efficient method for Stokes inversion for high resolution polarimetric observations obtained by GST/NIRIS.

arXiv comment24 pages, 9 figures

References in corpus (11)

• VFISV: Very Fast Inversion of the Stokes Vector for the Helioseismic and Magnetic Imager
• High-resolution observations of flare precursors in the low solar atmosphere
• Returning magnetic flux in sunspot penumbrae
• Stokes Inversion based on Convolutional Neural Networks
• Sparse inversion of Stokes profiles. I. Two-dimensional Milne-Eddington inversions
• Ultra-narrow Negative Flare Front Observed in Helium-10830~Å using the 1.6 m New Solar Telescope
• Strategies for Spectral Profile Inversion using Artificial Neural Networks
• Spatial deconvolution of spectropolarimetric data: an application to quiet Sun magnetic elements
• Zeeman-Tomography of the Solar Photosphere -- 3-Dimensional Surface Structures Retrieved from Hinode Observations
• Evolution of Photospheric Vector Magnetic Field Associated with Moving Flare Ribbons As Seen By GST
• Transient rotation of photospheric vector magnetic fields associated with a solar flare

Cited by in corpus (12)

• 2022 Review of Data-Driven Plasma Science
• A Magnetic Reconnection model for Hot Explosions in the Cool Atmosphere of the Sun
• DeepSun: Machine-Learning-as-a-Service for Solar Flare Prediction
• SynthIA: A Synthetic Inversion Approximation for the Stokes Vector Fusing SDO and Hinode into a Virtual Observatory
• Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds
• A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data
• Inferring Line-of-Sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
• The Ellerman bomb and Ultraviolet burst triggered successively by an emerging magnetic flux rope
• A nonlinear solar magnetic field calibration method for the filter-based magnetograph by the residual network
• Mapping the Sun's upper photosphere with artificial neural networks
• The MODEST catalog of depth-dependent spatially coupled inversions of sunspots observed by Hinode/SOT-SP
• A prototype of a large tunable Fabry-Perot interferometer for solar spectroscopy