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arXiv 0903.1008astro-ph.SR
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塞曼多普勒成像:老问题和新方法

Zeeman-Doppler Imaging : Old Problems and New Methods

T. A. Carroll, M. Kopf, K. G. Strassmeier, I. Ilyin

摘要:

塞曼多普勒成像(ZDI)是一种强有力的反演恒星表面磁场的方法。重建过程通常通过将逆问题转化为正则化最小二乘或优化问题来解决。在这篇文章中,我们将强调ZDI是一个固有的非线性问题,相应的正则化优化是,像许多非线性问题,可能倾向于局部极小值。我们将展示如何使用不充分的前向模型来加剧这个问题。为了促进更一致的充分辐射转移驱动的方法ZDI,我们描述了一个两阶段的战略,包括一个主成分分析(PCA)为基础的线轮廓重建和快速近似偏振辐射转移方法来合成本地斯托克斯配置文件。此外,我们介绍了一种新的统计反演方法,人工神经网络(ANN)的基础上,提供了一个快速计算的第一猜测模型,并允许将更好的物理约束到反演过程中。

英文摘要:

Zeeman-Doppler Imaging (ZDI) is a powerful inversion method to reconstruct stellar magnetic surface fields. The reconstruction process is usually solved by translating the inverse problem into a regularized least-square or optimization problem. In this contribution we will emphasize that ZDI is an inherent non-linear problem and the corresponding regularized optimization is, like many non-linear problems, potentially prone to local minima. We show how this problem will be exacerbated by using an inadequate forward model. To facilitate a more consistent full radiative transfer driven approach to ZDI we describe a two-stage strategy that consist of a principal component analysis (PCA) based line profile reconstruction and a fast approximate polarized radiative transfer method to synthesize local Stokes profiles. Moreover, we introduce a novel statistical inversion method based on artificial neural networks (ANN) which provide a fast calculation of a first guess model and allows to incorporate better physical constraints into the inversion process.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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