Investigations into new machine learning techniques for gravitational wave astronomy

引力波天文学新机器学习技术的研究

• 批准号: 2748216
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2748216
• 关键词: Investigations; into; new; machine; learning

The field of gravitational wave astronomy is currently benefiting from the rapid growth of machine learning applications to problems such as classification (detection or model selection) and regression (parameter estimation). So far, only these areas of gravitational wave data analysis have been investigated since there is a direct relation between them and the most common and well established machine learning processes. In this project we propose the investigation of a new (known) form of machine learning known as Physics Informed Neural Networks (PINNs) in which machine learning is used to obtain accurate models of functions that are particular solutions to physically informed problems. For example, such problems include gravitational waveforms modelled by Einsteins General Relativity, solutions to the Tolman Oppenheimer Volkoff Equation for neutron stars, solving geodesic paths for particles in a general relativistic spacetime, and solving the spacetime metric for arbitrary mass-energy distributions. The student will investigate the potential for PINNs amongst these and other problems in the gravitational wave field.

引力波天文学领域目前正受益于机器学习应用的快速增长，如分类（检测或模型选择）和回归（参数估计）。到目前为止，只有引力波数据分析的这些领域得到了研究，因为它们与最常见和最完善的机器学习过程之间存在直接关系。在这个项目中，我们提出了一种新的（已知的）机器学习形式的调查，称为物理信息神经网络（PINNs），其中机器学习用于获得精确的函数模型，这些模型是物理信息问题的特定解决方案。例如，这类问题包括爱因斯坦广义相对论所模拟的引力波形、托尔曼·奥本海默中子星方程的解、求解广义相对论时空中粒子的测地线路径，以及求解任意质能分布的时空度规。学生将研究PINN在引力波领域的这些和其他问题中的潜力。