水平马尔可夫链（MARCH）。I.将理论模型拟合到事件视界望远镜观测中的偏差识别
Markov Chains for Horizons (MARCH). I. Identifying Biases in Fitting Theoretical Models to Event Horizon Telescope Observations
摘要：
本文介绍一种新的并行回火马尔可夫链蒙特卡罗（MCMC）算法，用于拟合黑洞视界尺度图像的理论模型和事件视界望远镜（EHT）的干涉数据。该算法实现了数据中的噪声分布的形式，这些形式对于所有信噪比都是准确的。除了可并行化之外，该算法还针对高性能进行了优化，在单个处理器上在20秒内实现了100万个MCMC链步骤。我们使用基于分析以及广义相对论磁流体动力学（GRMHD）模型图像生成的2017年M87 EHT覆盖范围的合成数据，以探索拟合模型的几个潜在偏差来源稀疏干涉数据。我们证明了非常少的数据点，位于附近的显着特征的干涉数据施加不成比例的影响推断模型参数。我们还表明，EHT基线的首选方向在模型图像的方向的推断中引入了显着的偏差。最后，我们讨论的战略，帮助确定这种偏见的存在和严重程度在现实的应用程序。
英文摘要：
We introduce a new Markov Chain Monte Carlo (MCMC) algorithm with parallel tempering for fitting theoretical models of horizon-scale images of black holes to the interferometric data from the Event Horizon Telescope (EHT). The algorithm implements forms of the noise distribution in the data that are accurate for all signal-to-noise ratios. In addition to being trivially parallelizable, the algorithm is optimized for high performance, achieving 1 million MCMC chain steps in under 20 seconds on a single processor. We use synthetic data for the 2017 EHT coverage of M87 that are generated based on analytic as well as General Relativistic Magnetohydrodynamic (GRMHD) model images to explore several potential sources of biases in fitting models to sparse interferometric data. We demonstrate that a very small number of data points that lie near salient features of the interferometric data exert disproportionate influence on the inferred model parameters. We also show that the preferred orientations of the EHT baselines introduce significant biases in the inference of the orientation of the model images. Finally, we discuss strategies that help identify the presence and severity of such biases in realistic applications.
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