Gravitational-Wave Astronomy: From Theory to Data-Driven Discovery
Course Description:
This course provides a comprehensive introduction to the rapidly evolving field of gravitational-wave (GW) astronomy. We will begin with the theoretical foundations, deriving gravitational waves from the linearization of General Relativity and studying black hole perturbations. The core of the course will then pivot to the data analysis techniques that make modern GW science possible, including statistical inference, numerical data analysis tools, and an introduction to machine learning applications. Finally, we will explore how GW observations are used to perform precision tests of General Relativity in the strong-field regime and will conclude with a look at the low-frequency GW universe through Pulsar Timing Arrays. The course includes a significant practical component, with hands-on sessions using industry-standard Python libraries.
Learning Objectives:
Upon successful completion of this course, students will be able to:
Derive the properties of gravitational waves in the Lorenz and TT gauges.
Understand the concept of quasi-normal modes and perturbation theory for black holes.
Formulate and solve parameter estimation problems using both frequentist and Bayesian statistical frameworks.
Perform basic data analysis tasks on simulated GW data using PyCBC.
Evaluate the application of machine learning techniques, including symbolic regression, to astrophysical problems.
Critically assess tests of General Relativity using GW and electromagnetic observations.
Describe the methodology and scientific goals of Pulsar Timing Array experiments.
This course provides a comprehensive introduction to the rapidly evolving field of gravitational-wave (GW) astronomy. We will begin with the theoretical foundations, deriving gravitational waves from the linearization of General Relativity and studying black hole perturbations. The core of the course will then pivot to the data analysis techniques that make modern GW science possible, including statistical inference, numerical data analysis tools, and an introduction to machine learning applications. Finally, we will explore how GW observations are used to perform precision tests of General Relativity in the strong-field regime and will conclude with a look at the low-frequency GW universe through Pulsar Timing Arrays. The course includes a significant practical component, with hands-on sessions using industry-standard Python libraries.
Learning Objectives:
Upon successful completion of this course, students will be able to:
Derive the properties of gravitational waves in the Lorenz and TT gauges.
Understand the concept of quasi-normal modes and perturbation theory for black holes.
Formulate and solve parameter estimation problems using both frequentist and Bayesian statistical frameworks.
Perform basic data analysis tasks on simulated GW data using PyCBC.
Evaluate the application of machine learning techniques, including symbolic regression, to astrophysical problems.
Critically assess tests of General Relativity using GW and electromagnetic observations.
Describe the methodology and scientific goals of Pulsar Timing Array experiments.
讲师
日期
2025年09月22日 至 12月22日
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周一,周三 | 13:30 - 15:05 | A14-101 | Zoom 17 | 442 374 5045 | BIMSA |
修课要求
Software Installation: Python, Jupyter Notebooks, PyCBC, PySINDy, PySR.
课程大纲
Introduction: The Linearized Theory of GR
The Transverse-Traceless Gauge and Wave Generation
Black Hole Perturbations & Ringing in Spacetime
GW Astrophysics Overview
The Statistician's Toolkit I: Frequentist Methods
The Statistician's Toolkit II: Bayesian Inference
Hands-On GW Data Analysis
Introduction to Machine Learning for Science I
Introduction to Machine Learning for Science II (Data-Driven Discovery: PySR & PySINDy)
Tests of General Relativity I: The Classical Tests
Tests of General Relativity II: The GW Frontier
The Low-Frequency Universe: Pulsar Timing Arrays
The Transverse-Traceless Gauge and Wave Generation
Black Hole Perturbations & Ringing in Spacetime
GW Astrophysics Overview
The Statistician's Toolkit I: Frequentist Methods
The Statistician's Toolkit II: Bayesian Inference
Hands-On GW Data Analysis
Introduction to Machine Learning for Science I
Introduction to Machine Learning for Science II (Data-Driven Discovery: PySR & PySINDy)
Tests of General Relativity I: The Classical Tests
Tests of General Relativity II: The GW Frontier
The Low-Frequency Universe: Pulsar Timing Arrays
参考资料
General Relativity by Robert M. Wald
PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals, C. M. Biwer et al.
E. S. Phinney. A Practical theorem on gravitational wave backgrounds. 7 2001.
G. Hobbs, F. Jenet, K. J. Lee, J. P. W. Verbiest, D. Yardley, R. Manchester, A. Lommen, W. Coles, R. Edwards, and C. Shettigara. tempo2: a new pulsar timing package iii. gravitational wave simulation. Monthly Notices of the Royal Astronomical Society, 394(4):1945–1955, April 2009.
Cranmer, Miles. "Interpretable machine learning for science with PySR and SymbolicRegression. jl." arXiv preprint arXiv:2305.01582 (2023).
Kaptanoglu, Alan A., et al. "PySINDy: A comprehensive Python package for robust sparse system identification." arXiv preprint arXiv:2111.08481 (2021).
PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals, C. M. Biwer et al.
E. S. Phinney. A Practical theorem on gravitational wave backgrounds. 7 2001.
G. Hobbs, F. Jenet, K. J. Lee, J. P. W. Verbiest, D. Yardley, R. Manchester, A. Lommen, W. Coles, R. Edwards, and C. Shettigara. tempo2: a new pulsar timing package iii. gravitational wave simulation. Monthly Notices of the Royal Astronomical Society, 394(4):1945–1955, April 2009.
Cranmer, Miles. "Interpretable machine learning for science with PySR and SymbolicRegression. jl." arXiv preprint arXiv:2305.01582 (2023).
Kaptanoglu, Alan A., et al. "PySINDy: A comprehensive Python package for robust sparse system identification." arXiv preprint arXiv:2111.08481 (2021).
听众
Undergraduate
, Advanced Undergraduate
, Graduate
, 博士后
, Researcher
视频公开
不公开
笔记公开
不公开
语言
英文
讲师介绍
Jahed Abedi is a black hole physicist with a broad interest in gravitational physics, bridging both observational and theoretical domains. On the observational side, his work focuses on the search for gravitational wave (GW) echoes and Quasi-Normal Modes (QNMs) in LIGO/Virgo data, while his theoretical research delves into black hole perturbations, QNMs, and Quantum Field Theory (QFT) in curved space-time. Jahed was awarded the 2019 Buchalter Cosmology First Prize for one of his publications where he served as the lead author, reflecting the high impact of his research. He holds a Bachelor's degree in Electrical Engineering, as well as a Master's and PhD in Physics. His research seeks to answer several critical questions: How can a better pipeline be developed to test the Kerr nature of observed Binary Black Hole Mergers through black hole spectroscopy? With improved methods, can additional subdominant Quasi-Normal Modes (QNMs) be detected? Can these results validate previous searches or reveal deviations from General Relativity in GW data? What quantum effects might be expected from black holes, and if they exist, how significant are they? Can such effects be observed? Lastly, how can gravitational wave data confirm or rule out alternatives to classical black holes or their mimickers? Jahed's work continues to push the frontiers of black hole physics, and he remains open to collaborations and inquiries from those interested in his research.