  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.
