论文标题
Gallifray-使用贝叶斯技术的黑洞图像的几何建模和参数估计框架
GALLIFRAY -- A geometric modeling and parameter estimation framework for black hole images using bayesian techniques
论文作者
论文摘要
最近，使用非常长的基线干涉法（VLBI），对M87的银河系中心和事件地平线望远镜的银河系进行了观察到了基础物理基础物理学的新时代。作为一个新生的领域，Vogue中有几种不同的建模和分析方法（例如，几何和物理模型，可见性和闭合振幅，不可知论和多通用剂先验）。我们提出\ texttt {gallifray}，这是一种基于开源Python的框架，用于使用VLBI数据估算参数。它以模块化，效率和适应性为主要目标开发。本文概述了\ texttt {gallifray}的设计和用法。作为例证，我们拟合了使用马尔可夫链蒙特卡洛采样的几何和物理模型来模拟数据集，并找到后验分布的良好收敛性。我们以目前正在开发的进一步增强的概述结束。
Recent observations of the galactic centers of M87 and the Milky Way with the Event Horizon Telescope have ushered in a new era of black hole based tests of fundamental physics using very long baseline interferometry (VLBI). Being a nascent field, there are several different modeling and analysis approaches in vogue (e.g., geometric and physical models, visibility and closure amplitudes, agnostic and multimessenger priors). We present \texttt{GALLIFRAY}, an open-source Python-based framework for estimation/extraction of parameters using VLBI data. It is developed with modularity, efficiency, and adaptability as the primary objectives. This article outlines the design and usage of \texttt{GALLIFRAY}. As an illustration, we fit a geometric and a physical model to simulated datasets using markov chain monte carlo sampling and find good convergence of the posterior distribution. We conclude with an outline of further enhancements currently in development.
