NRSurCat-1

NRSurCat-1#

We present, NRSurCat-1, the catalog of posterior samples associated with the paper “Analysis of GWTC-3 with fully precessing numerical relativity surrogate models”, Islam et al, 2023. This includes 47 binary black hole gravitational wave events (from 2015-2020, LVK O1-O3) analyzed using the NRSur7dq4 and NRSur7dq4Remnant models.

This website contain plots of NRSurCat-1 posteriors and example code for downloading and interacting with the results. The pages in this website are:

NRSurrogate Events: Contains a list of all analysed events with links to their pages, containing corner plots and animations for the events, such as the following animations for GW150914.

Catalog plots: Demonstrates how to load the entire catalog and make plots.
API documentation: Describes the python and command-line interface to the catalog package.

Data availability#

The posterior samples are available on Zenodo and can also be downloaded using the python-API, nrsur_catalog:

Using CLI

! pip install nrsur_catalog
get_nrsur_event --event-name GW150914_095045 
get_nrsur_event --all

See more on the catalog API page.

Using py

Load samples from one event with

from nrsur_catalog import NRsurResult

nrsur_result = NRsurResult.load("GW150914_095045")

For example, refer to the page for GW150914_095045.

Load samples from all events with

from nrsur_catalog import Catalog

catalog = Catalog.load()

See more on the catalog plots page.

Citation#

If you make use of the NRSurCat-1, please cite this work and its dependencies.

@article{Islam:2023zzj,
    title = {
		{Analysis of GWTC-3 with fully
		precessing numerical relativity
		surrogate models}
	},
    author = {
		Islam, Tousif and Vajpeyi, Avi and
		Shaik, Feroz H. and Haster,
		Carl-Johan and Varma, Vijay and
		Field, Scott E. and Lange, Jacob and
		O'Shaughnessy, Richard and Smith,
		Rory
	},
    year = 2023,
    month = 9,
    eprint = {2309.14473},
    archiveprefix = {arXiv},
    primaryclass = {gr-qc}
}

License & attribution#

All analyses were performed with ❤️ using:

The source code is made available under the terms of the MIT license.

Website acknowledgements#

This website was generated using Jupyter book and code from Dan Foreman-Mackey’s TESS Atlas project.