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arXiv 2210.14933astro-ph.SRastro-ph.IMphysics.data-anphysics.ins-detphysics.space-ph
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神经网络Stokes反演技术:参数估计的不确定性分析

Stokes inversion techniques with neural networks: analysis of uncertainty in parameter estimation

Lukia Mistryukova, Andrey Plotnikov, Aleksandr Khizhik, Irina Knyazeva, Mikhail Hushchyn, Denis Derkach

摘要:

磁场是造成许多太阳现象的原因,包括太阳耀斑和日冕物质抛射等破坏性事件,随着我们接近11年太阳活动周期的高峰,大约在2025年,这些事件的数量会增加。高精度的分光偏振观测对于了解太阳的变化是必要的。从这种观测中定量推断磁场矢量和相关的太阳大气参数的领域早已被研究过。近年来,已经开发出非常复杂的分光偏振观测代码。在过去的二十年里,神经网络已被证明是一种快速和准确的替代经典反演技术方法。然而,这些代码中的大多数可以用来获得点估计的参数,所以模糊性,退化,和每个参数的不确定性仍然未被发现。在本文中,我们提供端到端的反演代码的基础上简单的米尔恩-爱丁顿模型的恒星大气和深度神经网络的参数估计和他们的不确定性区间。拟议的框架的设计方式,它可以扩展和适应其他大气模型或它们的组合。附加信息也可以直接合并到模型中。它表明,所提出的架构提供了高精度的结果,包括可靠的不确定性估计,即使在多维的情况下。使用模拟和真实数据样本对模型进行了测试。

英文摘要:

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 been developed. Over the past two decades, neural networks have been shown to be a fast and accurate alternative to classic inversion technique methods. However, most of these codes can be used to obtain point estimates of the parameters, so ambiguities, the degeneracies, and the uncertainties of each parameter remain uncovered. In this paper, we provide end-to-end inversion codes based on the simple Milne-Eddington model of the stellar atmosphere and deep neural networks to both parameter estimation and their uncertainty intervals. The proposed framework is designed in such a way that it can be expanded and adapted to other atmospheric models or combinations of them. Additional information can also be incorporated directly into the model. It is demonstrated that the proposed architecture provides high accuracy of results, including a reliable uncertainty estimation, even in the multidimensional case. The models are tested using simulation and real data samples.

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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