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

论文当前的工作标题是“高能天体物理学中的贝叶斯方法和随机变异性建模”。

• 批准号: 2283474
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2283474
• 关键词: current; working; title; thesis; Bayesian

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.

本论文的目的是回顾、应用和扩展当前天文数据贝叶斯统计建模的方法。特别是，这项研究的主要重点是类星体或X射线双星等高能天体物理源的时间变异性建模。在这一主题下，该项目的结构将整合两个主要的研究子组成部分。第一个分组成部分将试图解决天文学来源重叠的问题，目前正在开发的方法在统计卫星图像处理方面具有潜在的应用。天体物理学界担心SpaceX的卫星星座会污染备受期待的Vera C的空间和时间遗产调查（LSST）产生的图像。鲁宾天文台。该方法可用于对LSST图像的卫星拖尾进行预处理，为后续分析提供清晰的图像。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，并开发一种强大的工具，使天体物理学家可以将其集成到当前的研究过程中。第二子部分将试图通过提供对这一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。这涉及开发灵活和可扩展的方法来模拟天文时间序列。研究方法的新奇在于设计最先进的建模和计算技术，从复杂的数据中获得新的见解。这项研究的直接影响将是对天文数据的统计分析，但开发的方法被认为是高度概括性的，将适用于许多不同的领域。该项目属于EPSRC“统计和应用概率"研究领域的福尔斯。该项目由伦敦帝国学院的大卫货车戴克教授监督，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。