The current working title of the thesis is "Bayesian Methods and Stochastic Variability Modelling in High-Energy Astrophysics".

本文的目的是回顾、应用和扩展现有的贝叶斯统计建模方法。特别是，本研究的主要焦点是类星体或x射线双星等高能天体物理源的时间变异性建模。在这个主题下，项目的结构将整合两个主要的研究子组件。第一个子组件将尝试解决重叠天文源的问题，目前正在开发的方法在统计卫星图像处理中具有潜在的应用前景。天体物理学家担心SpaceX的卫星群会污染备受期待的维拉·c·鲁宾天文台（Vera C. Rubin Observatory）遗留时空调查（LSST）所产生的图像。该方法可以对LSST影像的卫星尾迹进行预处理，为后续分析提供清晰的影像。除了改进和扩展目前用于解决这一问题的模型外，该项目还专注于设计高效和可扩展的算法，并开发一个强大的工具，天体物理学家可以将其集成到当前的研究过程中。第二个子组件将尝试通过提供这个感兴趣的物理量的概率估计来解决哈勃常数测量不一致的问题。这包括开发灵活和可扩展的方法来模拟天文时间序列。研究方法的新颖性在于最先进的建模和计算技术的设计，以从复杂的数据中得出新的见解。这项研究的直接影响将是对天文数据的统计分析，但所开发的方法被认为是高度一般化的，将适用于许多不同的领域。该项目属于EPSRC“统计与应用概率研究领域”。该项目由David van Dyk教授（伦敦帝国理工学院）监督，哈佛-史密森天体物理中心的一些天体物理学家也参与其中。

The aim of this thesis is to review, apply and extend current methods in Bayesian statistical modelling for astronomical data. In particular, the primary focus of this research is the modelling of the time variability of high-energy astrophysical sources such as quasars or X-ray binaries. Under this theme, the structure of the project will integrate two main research sub-components. The first sub-component will attempt to solve the problem of overlapping astronomical sources, and the method currently under development has potential applications in statistical satellite imagery processing. The astrophysics community is worried that the SpaceX satellite constellation will pollute the imagery produced by the much anticipated Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory. This method could be used to pre-process satellite trails off the LSST imagery and provide clean images for follow-up analysis. In addition to improving and extending the models currently used to solve this problem, the project also focuses on designing highly efficient and scalable algorithms and developing a powerful tool that astrophysicists can integrate in their current research processes. The second sub-component will attempt to solve the problem of the inconsistent Hubble constant measurements by providing a probabilistic estimation of this physical quantity of interest. This involves developing flexible and scalable methods to model astronomical time series. The novelty of the research methodology resides in the design of state-of-the-art modelling and computational techniques to draw new insights from complex data. The immediate impact of this research will be on statistical analysis of astronomical data, but the developed methods are conceived to be highly generalizable and will be applicable to many different fields. This project falls within the EPSRC "Statistics and Applied Probability research area. This project is supervised by Prof. David van Dyk (Imperial College London), and a number of astrophysicists from the Harvard-Smithsonian Center for Astrophysics are also involved.