Fast Parameter Estimation of Binary Mergers for Multimessenger Followup

Daniel Finstad¹, Duncan A. Brown¹

¹Department of Physics, Syracuse University, Syracuse, NY 13244, USA

License

This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 United States License.

Introduction

This notebook is a companion to Finstad & Brown (2020) which is posted at arxiv:2009.13759. It demonstrates how to read and use our posterior proability density files from the parameter estimation and shows how in principle to reconstruct Figure 3 in the paper from the raw data.

We encourage use of these data in derivative works.

The data provided contain the posterior samples from a selection of the simulated binary neutron star and neutron star--black hole signals recovered by our analysis. These data are stored in the files contained in bns_posteriors for binary neutron star signals, and nsbh_posteriors for neutron star--black hole signals. The notebook data_release_companion.ipynb contains a demonstration of retrieving posterior samples from the data files and plotting the results shown in Figure 3 of the paper.

The results used in the paper were generated with the PyCBC v1.16.9 release.

Runing this notebook in a Docker container

This notebook can be run from a PyCBC Docker container, or a machine with PyCBC installed. Instructions for downloading the docker container are available from the PyCBC home page. To start a container with instance of Jupyter notebook, run the commands

docker pull pycbc/pycbc-el7:v1.16.9
docker run -p 8888:8888 --name pycbc_notebook -it pycbc/pycbc-el7:v1.16.9 /bin/bash -l

Once the container has started, this git repository can be downloaded with the command:

git clone https://github.com/sugwg/rapid-relbin-pe.git

The notebook server can be started inside the container with the command:

jupyter notebook --ip 0.0.0.0 --no-browser

You can then connect to the notebook server at the URL printed by jupyter. Navigate to the directory rapid-relbin-pe in the cloned git repository and open data_release_companion.ipynb, the notebook that demonstrates use of these reults.

Acknowledgements

We thank Brian Metzger, Ben Margalit, and Edo Berger for helpful discussions. DAB thanks the Kavli Institute for Theoretical Physics for their hospitality.

Funding

This work was supported by U.S. National Science Foundation Grant No. PHY-1707954 and PHY-2011655. Computational work was supported by Syracuse University and National Science Foundation award OAC-1541396. DAB received partial support from the Kavli Institute for Theoretical Physics which is supported by the National Science Foundation award PHY-1748958.

Authors contributions:

Conceptualization, DAB and DF; Methodology, DAB and DF; Software: DF; Formal Analysis: DF; Investigation: DF; Resources: DAB; Writing – Original Draft: DF; Writing – Review and Editing: DAB and DF; Visualization: DF; Supervision: DAB; Project Administration: DAB; Funding Acquisition: DAB.