利用贝叶斯推断估计全球日冕磁场
Global Coronal Magnetic Field Estimation Using Bayesian Inference
AI总结：
针对日冕磁场难以直接测量的问题，本研究利用CoMP观测数据，通过贝叶斯推断结合两种密度先验分布得到1.05-1.35倍太阳半径的全球日冕磁场分布，发现其径向变化符合幂律，且简单反演结果与贝叶斯最大后验估计存在差异。
摘要：
估计日冕中的磁场强度对于理解不同时空尺度上发生的各类物理过程至关重要。然而，日冕的高温和低密度使得这项任务极具挑战性。日冕磁场过弱，无法通过塞曼效应产生可测量的谱线分裂，而高温又会导致谱线变弱且变宽，使得微小的塞曼分裂难以探测。日冕磁震学结合了磁流体动力学（MHD）波的理论特性与观测特性，可用于推断日冕中振荡结构的磁场强度，而这类结构的磁场原本难以估计。在本研究中，我们利用2016年10月14日由日冕多通道偏振仪（CoMP）获取的多普勒速度和密度数据，通过贝叶斯推断得到了日冕磁场的全球分布图。我们对等离子体密度采用了两种先验分布，即高斯分布和均匀分布。贝叶斯推断为我们提供了1.05至1.35倍太阳半径（$R_\u2299$）范围内每个位置的磁场强度概率分布。我们还对比了通过简单反演和贝叶斯推断得到的磁场结果。研究发现，通过简单反演得到的数值并不总是与贝叶斯推断得到的最大后验估计值一致。我们还发现，推断得到的日冕磁场径向变化符合幂律函数，且简单反演与贝叶斯反演得到的幂律指数相近。
英文摘要：
Estimating the magnetic field strength in the solar corona is crucial for understanding different physical processes happening over diverse spatio-temporal scales. However, the high temperatures and low density of the solar corona make this task challenging. The coronal magnetic field is too weak to produce a measurable splitting of the spectral lines using the Zeeman effect, and high temperature causes spectral lines to become weak and broad, making it difficult to detect the small Zeeman splitting. Coronal magneto-seismology, which combines the theoretical and observed properties of magnetohydrodynamic (MHD) waves, can be used to infer the magnetic field strength of oscillating structures in the solar corona, which are otherwise difficult to estimate. In this work, we use the Doppler velocity and density data obtained from the Coronal Multichannel Polarimeter (CoMP) on 2016 October 14 to obtain the global map of the coronal magnetic field using Bayesian inference. Two priors are used for plasma density, viz Gaussian and uniform distributions. Bayesian inference provides us with the probability distribution for the magnetic field strength at each location from 1.05 to 1.35 $R_\odot$. A comparison between the magnetic field obtained using simple inversion and Bayesian inference is also drawn. We find that the values obtained using simple inversion do not always match the maximum posterior estimates obtained using Bayesian inference. We find that the inferred values follow a power-law function for the radial variation of the coronal magnetic field, with the power-law indices for simple and Bayesian inversion being similar.
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