Bayesian analysis of black hole ringdowns. The original paper that inspired this code package is Isi, et al. (2019); a full description of the code and method can be found in Isi & Farr (2021).

This package is pip installable:

pip install ringdown

For the latest and greatest version, you can install directly from the git repo:

pip install git+https://github.com/maxisi/ringdown.git

A complete conda environment that includes all the prerequisites (and more!) to install ringdown can be found in environment.yml in the current directory:

conda env create -f environment.yml
conda activate ringdown
pip install ringdown

will leave the shell in an environment that includes jupyterlab ready to explore the ringdown package.

The environment.yml file enables running ringdown in JupyterHub services like MyBinder by pointing MyBinder at this repository or clicking the button at the top of this README. (Don't forget to pip install ringdown after the binder activates!)

See the docs/examples directory for Jupyter notebooks that give examples of using the package. In particular, docs/examples/GW150914.ipynb demonstrates an analysis of the ringdown in GW150914 and uses the fundamental (2,2) mode and first overtone to constrain the Kerr-ness of the post-peak signal, much like Isi, et al. (2019).

We ask that scientific users of this code cite the corresponding Zenodo entry (see blue DOI badge above), as well as Isi & Farr (2021):

@article{Isi:2021iql,
    author = "Isi, Maximiliano and Farr, Will M.",
    title = "{Analyzing black-hole ringdowns}",
    eprint = "2107.05609",
    archivePrefix = "arXiv",
    primaryClass = "gr-qc",
    reportNumber = "LIGO-P2100227",
    month = "7",
    year = "2021"
}