Gravitational Wave Approximate LiKelihood (GWALK)

Library for fitting approximate likelihood functions for Gravitational Wave events, with methods applicable in general for modeling sample-based distributions.

Specifically, the Normal Approximate Likelihood (NAL) models are optimized, bounded (truncated) multivariate normal distributions.

The non-parametric methods included also include density estimation as marginalized Gaussian process estimates.

See the associated data release: nal-data.

See also basil-core, csrk-py.

Citation

@misc{https://doi.org/10.48550/arxiv.2205.14154,
  doi = {10.48550/ARXIV.2205.14154},
  url = {https://arxiv.org/abs/2205.14154},
  author = {Delfavero, Vera and O'Shaughnessy, Richard and Wysocki, Daniel and Yelikar, Anjali},
  keywords = {Instrumentation and Methods for Astrophysics (astro-ph.IM), General Relativity and Quantum Cosmology (gr-qc), FOS: Physical sciences, FOS: Physical sciences},
  title = {Compressed Parametric and Non-Parametric Approximations to the Gravitational Wave Likelihood},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

Installation:

Method 1:

This may only work with python 3.12 or newer, and requires the Rust compiler cargo to be installed on a computer.

python3 -m pip install gwalk

Method 2:

This should work on any computer with anaconda:

conda create --name gwalk python=3.12
conda activate gwalk
conda install numpy==2.3.5
conda install h5py
conda install rust
pip install gwalk
python3 -m pip install --upgrade ipykernel
python3 -m ipykernel install --user --name "gwalk" --display-name "gwalk" # For jupyter 

Contributing

We are open to pull requests.

If you would like to make a contribution, please explain what changes you are making and why.

License

MIT