bilby-nr 0.1.0a1

Python package to extend the functionality of `bilby` to incorporate model accuracy into gravitational wave Bayesian analyses

bilby NR

Development status

This Python package incorporates model accuracy into gravitational wave Bayesian analyses via the bilby Python package.

Installation

bilby_nr is currently available via PyPI and can be installed with:

$ pip install bilby_nr

Once bilby_nr has been installed, a custom version of bilby_pipe needs to be installed with:

$ pip install 'bilby_pipe @ git+https://git.ligo.org/charlie.hoy/bilby_pipe.git@input_class'

This version needs to be installed because we are waiting for required code to be merged into the main bilby_pipe code base. Please see the following merge request for details:

* `bilby_pipe!583 <https://git.ligo.org/lscsoft/bilby_pipe/-/merge_requests/583>`_

For full installation instructions, see our documentation.

Usage in bilby_pipe

The functionality in bilby_nr can be used with bilby_pipe as you would with any other frequency domain source model. It simply requires the following options to be specified in your configuration file:

analysis_executable_parser=bilby_nr.bilby_pipe.create_parser
waveform-approximant=IMRPhenomXPHMST,IMRPhenomTPHM,SEOBNRv5PHM
frequency-domain-source-model = bilby_nr.source.multi_model_binary_black_hole
waveform-arguments-dict={'match_interpolant': 'bilby_nr.match.match_from_pade_pade_interpolant'}

Citing

If you find bilby_nr useful in your work please cite the following papers:

@article{Hoy:2024vpc,
    author = "Hoy, Charlie and Akcay, Sarp and Mac Uilliam, Jake and Thompson, Jonathan E.",
    title = "{Incorporation of model accuracy in gravitational wave Bayesian inference}",
    eprint = "2409.19404",
    archivePrefix = "arXiv",
    primaryClass = "gr-qc",
    reportNumber = "LIGO-P2400393",
    doi = "10.1038/s41550-025-02579-7",
    journal = "Nature Astron.",
    volume = "9",
    number = "8",
    pages = "1256--1267",
    year = "2025"
}

@article{Hoy:2022tst,
    author = "Hoy, Charlie",
    title = "{Accelerating multimodel Bayesian inference, model selection, and systematic studies for gravitational wave astronomy}",
    eprint = "2208.00106",
    archivePrefix = "arXiv",
    primaryClass = "gr-qc",
    reportNumber = "LIGO-P2200228",
    doi = "10.1103/PhysRevD.106.083003",
    journal = "Phys. Rev. D",
    volume = "106",
    number = "8",
    pages = "083003",
    year = "2022"
}