A Bayesian framework has been used to improve the quality of inferred plasma parameter profiles. An integrated data analysis allows for coherent combinations of different diagnostics, and Gaussian process regression provides a reliable regularization process and systematic uncertainty estimation. In this paper, we propose a new profile inference framework that utilizes our prior knowledge about plasma physics, along with integrated data analysis and a Gaussian process. In order to facilitate the use of the Markov chain Monte Carlo sampling, we use a Gaussian process to define quantities corresponding to the second derivatives of the profiles. We validate the analysis technique by using a synthetic one-dimensional plasma, in which the transport properties are known and demonstrate that the proposed analysis technique can infer plasma parameter profiles from line-integrated measurements only. Furthermore, we can even infer unknown parameters in our physics models when our physics knowledge on the system is incomplete. This analysis framework is applicable to laboratory plasmas and provides a means to investigate plasma parameters, to which standard diagnostics are not directly sensitive.

中文翻译：

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利用二阶导数先验和基于物理的约束从有限的数据推断血浆参数分布

贝叶斯框架已被用于改善推断的血浆参数分布图的质量。集成的数据分析可实现不同诊断程序的连贯组合，而高斯过程回归可提供可靠的正则化过程和系统的不确定性估计。在本文中，我们提出了一个新的轮廓推断框架，该框架利用了我们对等离子体物理学的先验知识，以及集成的数据分析和高斯过程。为了便于使用马尔可夫链蒙特卡洛采样，我们使用高斯过程来定义与轮廓的二阶导数相对应的量。我们通过使用合成的一维等离子体验证了分析技术，其中的传输特性是已知的，并证明了所提出的分析技术只能从线积分测量中推断出血浆参数曲线。此外，当我们对系统的物理知识不完整时，我们甚至可以推断出物理模型中的未知参数。该分析框架适用于实验室血浆，并提供了一种对血浆参数进行调查的方法，而标准诊断对这些参数并不直接敏感。