Inferring Line-of-Sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks

作     者：Jiang, Haodi Li, Qin Xu, Yan Hsu, Wynne Ahn, Kwangsu Cao, Wenda Wang, Jason T.L. Wang, Haimin 

作者机构：Institute for Space Weather Sciences New Jersey Institute of Technology University Heights NewarkNJ07102-1982 United States Department of Computer Science New Jersey Institute of Technology University Heights NewarkNJ07102-1982 United States Center for Solar-Terrestrial Research New Jersey Institute of Technology University Heights NewarkNJ07102-1982 United States Big Bear Solar Observatory New Jersey Institute of Technology 40386 North Shore Lane Big Bear CityCA92314-9672 United States Institute of Data Science National University of Singapore Singapore119077 Singapore Department of Computer Science School of Computing National University of Singapore Singapore119077 Singapore 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Deep neural networks 

摘      要：Obtaining high-quality magnetic and velocity fields through Stokes inversion is crucial in solar physics. In this paper, we present a new deep learning method, named Stacked Deep Neural Networks (SDNN), for inferring line-of-sight (LOS) velocities and Doppler widths from Stokes profiles collected by the Near InfraRed Imaging Spectropolarimeter (NIRIS) on the 1.6 m Goode Solar Telescope (GST) at the Big Bear Solar Observatory (BBSO). The training data of SDNN is prepared by a Milne–Eddington (ME) inversion code used by BBSO. We quantitatively assess SDNN, comparing its inversion results with those obtained by the ME inversion code and related machine learning (ML) algorithms such as multiple support vector regression, multilayer perceptrons and a pixel-level convolutional neural network. Major findings from our experimental study are summarized as follows. First, the SDNN-inferred LOS velocities are highly correlated to the ME-calculated ones with the Pearson product-moment correlation coefficient being close to 0.9 on average. Second, SDNN is faster, while producing smoother and cleaner LOS velocity and Doppler width maps, than the ME inversion code. Third, the maps produced by SDNN are closer to ME’s maps than those from the related ML algorithms, demonstrating the better learning capability of SDNN than the ML algorithms. Finally, comparison between the inversion results of ME and SDNN based on GST/NIRIS and those from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory in flare-prolific active region NOAA 12673 is presented. We also discuss extensions of SDNN for inferring vector magnetic fields with empirical evaluation. Copyright © 2022, The Authors. All rights reserved.