Applications of machine learning for gravitational wave astrophysics

该项目的目的是应用和开发机器学习技术来解决与引力波天体物理学相关的数据分析问题。该学生将首先解决紧凑二进制合并检测问题，并展示深度神经网络可以用来复制传统匹配滤波方法的性能。接下来将研究对良好建模的引力波信号进行参数估计的可行性，目的是将机器学习与最优贝叶斯方法进行比较。有了这些工具，学生将把新的机器学习工具扩展到其他模型引力波信号和探测器（例如，第三代和天基）。此外，学生将研究使用无监督（或弱监督）机器学习算法来搜索和表征未建模的瞬态引力波信号（爆发）。

The aim of the project is to apply and develop machine learning techniques to the data analysis problems associated with gravitational wave astrophysics. The student will initially tackle the compact binary coalescence detection problem and show that a deep neural network can be used to replicate the performance of traditional matched-filtering approaches. This will be followed by an investigation into the feasibility of performing parameter estimation for well modelled gravitational wave signals with the aim of comparing the machine learning with the optimal Bayesian approach. With these tools in hand the student will then extend the new machine learning tools to other modelled gravitational wave signals and detectors (e.g., 3rd generation and space-based). In addition the student will investigate the use of un-supervised (or weakly supervised) machine learning algorithms for the search and characterisation of un-modelled transient gravitational wave signals (bursts).