Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
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DOI：
10.3847/1538-4357/ac927e
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发表时间：
2022-10
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通讯作者：
Haodi Jiang;Qin Li;Yan Xu;W. Hsu;K. Ahn;W. Cao;J. T. Wang;Haimin Wang
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作者：
Haodi Jiang;Qin Li;Yan Xu;W. Hsu;K. Ahn;W. Cao;J. T. Wang;Haimin Wang
通过Stokes反演获得高质量的磁场和速度场是太阳物理研究的关键。在本文中，我们提出了一种新的深度学习方法，称为堆叠深度神经网络（SDNN），用于从大熊太阳天文台（BBSO）的1.6 m Goode太阳望远镜（GST）上的近红外成像光谱仪（NIRIS）收集的Stokes轮廓推断视距（LOS）速度和多普勒宽度。SDNN的训练数据由BBSO使用的Milne-Eddington （ME）反演码准备。我们定量地评估SDNN，将其反演结果与ME反演代码和相关机器学习（ML）算法（如多重支持向量回归、多层感知器和像素级卷积神经网络）获得的结果进行比较。实验研究的主要发现如下：首先，sdn推断的LOS速度与me计算的LOS速度高度相关，Pearson积矩相关系数平均接近0.9。其次，与ME反演代码相比，SDNN更快，同时生成更平滑、更清晰的LOS速度和多普勒宽度图。第三，SDNN生成的地图比相关ML算法生成的地图更接近于ME的地图，说明SDNN的学习能力优于ML算法。最后，将基于GST/NIRIS的ME和SDNN反演结果与耀斑活跃区NOAA 12673太阳动力学观测台日震和磁成像仪反演结果进行了比较。我们还讨论了用经验评价来推断矢量磁场的SDNN的扩展。
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 for SDNN are 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 that the learning capability of SDNN is better than those of the ML algorithms. Finally, a 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.