Inversion codes are numerical tools used for the inference of physical properties from the observations. Despite their success, the quality of current spectropolarimetric observations and those expected in the near future presents a challenge to current inversion codes. The pixel-by-pixel strategy of inverting spectropolarimetric data that we currently utilize needs to be surpassed and improved. The inverted physical parameters have to take into account the spatial correlation that is present in the data and that contains valuable physical information. We utilize the concept of sparsity or compressibility to develop an new generation of inversion codes for the Stokes parameters. The inversion code uses numerical optimization techniques based on the idea of proximal algorithms to impose sparsity. In so doing, we allow for the first time to exploit the presence of spatial correlation on the maps of physical parameters. Sparsity also regularizes the solution by reducing the number of unknowns. We compare the results of the new inversion code with pixel-by-pixel inversions, demonstrating the increase in robustness of the solution. We also show how the method can easily compensate for the effect of the telescope point spread function, producing solutions with an enhanced contrast.
The Milne-Eddington approximation provides an analytic and simple solution to the radiative transfer equation. It can be easily implemented in inversion codes that are used to fit spectro-polarimetric observations to infer average values of the magnetic field vector and the line-of-sight velocity of the solar plasma. However, it is in principle restricted to spectral lines formed under local thermodynamic conditions. We show that a simple modification in the linear source function of the Milne-Eddington approximation is sufficient to infer relevant physical parameters from spectral lines that …
Limitations and biases in the retrieval of the polar magnetic field I: the role of the magnetic filling factor in Milne-Eddington inversions of simulated Hinode/SP data
Milne-Eddington inversions of the He I 10830 Å Stokes profiles: Influence of the Paschen-Back effect
Paschen-Back效应影响He I多重态在10830 μ m处的塞曼子能级，导致这些谱线塞曼分量的强度和位置发生变化。我们说明了这种效果的相关性，使用合成斯托克斯配置文件的He I 10830的多重线，并调查其对极化数据反演的影响。我们反演的特内里费岛红外偏振仪（TIP）在德国真空塔望远镜（VTT）获得的数据。我们比较的反演结果的基础上计算的合成配置文件和没有Paschen-Back效应被包括在内。我们发现，当考虑到不完整的Paschen-Back效应，平均16%的高场强值。我们还表明，这种效果是不是主要原因的面积不对称性所表现出的许多He I 10830斯托克斯V-配置文件。这表明在这些线的形成高度范围内速度和磁场梯度的重要性。
The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Optimization of the Spectral Line Inversion Code
斯托克斯矢量的快速反演（VFISV）是一个Milne-Eddington谱线反演程序，用于通过太阳动力学天文台（SDO）上的日震和磁成像仪（HMI）对6173 A Fe I谱线上的斯托克斯矢量的观测来确定太阳光球层的磁性和热力学参数。我们报告了对原始VFISV反演代码的修改，以优化其在HMI数据管道中的操作，并在活动区域提供最平滑的解决方案。这些变化要么加快了计算速度，要么降低了算法无法收敛到满意解的频率。此外，这里报告了在原始VFISV版本中检测到并修复的编码错误。
Stokes inversion techniques are very powerful methods for obtaining information on the thermodynamic and magnetic properties of solar and stellar atmospheres. In recent years, very sophisticated inversion codes have been developed that are now routinely applied to spectro-polarimetric observations. Most of these inversion codes are designed for finding an optimum solution to the nonlinear inverse problem. However, to obtain the location of potentially multimodal cases (ambiguities), the degeneracies, and the uncertainties of each parameter inferred from the inversions, algorithms such as Marko…
New Jersey Institute of Technology（新泽西理工学院）；National University of Singapore（新加坡国立大学）
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 th…
Electric currents play an important role in the energy balance of the plasma in the solar atmosphere. They are also indicative of non-potential magnetic fields and magnetic reconnection. Unfortunately, the direct measuring of electric currents has traditionally been riddled with inaccuracies. We study how accurately we can infer electric currents under different scenarios. We carry out increasingly complex inversions of the radiative transfer equation for polarized light applied to Stokes profiles synthesized from radiative three-dimensional magnetohydrodynamic (MHD) simulations. The inversion…
Magnetic fields are responsible for a multitude of Solar phenomena, including such destructive events as solar flares and coronal mass ejections, with the number of such events rising as we approach the peak of the 11-year solar cycle, in approximately 2025. High-precision spectropolarimetric observations are necessary to understand the variability of the Sun. The field of quantitative inference of magnetic field vectors and related solar atmospheric parameters from such observations has long been investigated. In recent years, very sophisticated codes for spectropolarimetric observations have…
Obtaining the magnetic field vector accurately in the solar atmosphere is essential for studying changes in field topology during flares and to reliably model space weather. We tackle this problem by applying various inversion methods to a confined X2.2 flare in NOAA AR 12673 on September 6, 2017, comparing the photospheric and chromospheric magnetic field vector with those from two numerical models of this event. We obtain the photospheric field from Milne-Eddington (ME) and (non-)local thermal equilibrium (non-LTE) inversions of Hinode SOT/SP Fe I 6301.5Å and 6302.5Å. The chromospheric field…
