- Overview
- Setting Up Environments and LIGO Account
- Python
- Parameter Estimation with MCMC
- Waveform Generation
- Suggested Papers
- Machine Learning in GW Astronomy
- Workshops and Schools
- GPUs
This repository collects essential steps for conducting research in gravitational-wave (GW) astronomy, based on our activities at the University of Minnesota (UMN).
During your educational-trip, I suggest you to have always open the ask-IGWN page where you can ask ANYTHING about GW!
A good start could be this page, in order to start thinking about the GW theory and distinguish some basic characteristics for frequencies, rate, masses etc. This page you provide you some educational links to better understand noise and data, and if you want more data to explore or "hear" here is your chance. Now, you have a main idea about what actually are Gravitational Waves and their data-challanges.
Want to know more about How does LIGO detect gravitational waves?, then click to this youtube video.
Detailed instructions for setting up your local machine and creating conda environments with the necessary packages for GW astronomy can be found here. Additionally, if you need to set up your LIGO/Virgo/KAGRA (LVK) account, follow the guidance provided here.
You may also want to take a look at bash introdactory.
The codes in this repository are developed using the Python programming language. If you're new to Python, there are plenty of online resources to help you get started! For a structured introduction, check out this organized repository: Python Introduction.
Parameter estimation is a crucial part of GW astronomy, often using techniques like Markov Chain Monte Carlo (MCMC). Bayesian Inference and MCMC is a repository that covers very basic concepts. More content for applications GW astronomy is to be added.
Waveform generation is fundamental for comparing theoretical models with observed GW data. This section will provide resources and examples to help you generate GW waveforms. We basically use two different packages, Bilby and PyCBC. You can find many different tutorials in the website of the mentioned packages. However, a quick overview could be achieved through these tutorials:
- BBH waveform generation and injection into gaussian noise using
Bilbypackage, A. Sasli (2024)- BBH waveform generation and injection into gaussian noise using
PyCBCpackage, A. Sasli (2024)- BNS waveform generation with
PyCBCpackage, A. Sasli (2024)- Waveforms Tutorial, P. Schmidt, G. Pratten (2021)
Note: I would suggest to take a look at all the above tutorials, because they might have some information that is not included in another tutorial!
For an in-depth understanding of GW research, reading the foundational and recent papers in the field is highly recommended. A curated list of suggested papers will be added here soon.
- Gravitational-Wave Astronomy, N. Andersson (2021)
- Gravitational Waves, Vol. 1, M. Maggiore (2007)
- Gravitational Waves, Vol. 2, M. Maggiore (2018)
- A guide to LIGO–Virgo detector noise and extraction of transient gravitational-wave signals and codes, Abbott et al. (2020)
- A Roadmap to Gravitational Wave Data Analysis, L. Speri et. al (2022)
- A very simple tutorial using
PyCBCfor BBH PE, can be found here, A. Sasli (2024).
Machine learning is becoming increasingly important in GW research for tasks such as signal classification, noise reduction, and parameter estimation. UMN and MIT group have developed tools and ML algorithms for such purposes. For more details, please visit ML4GW repository