precession 1.0.2

Dynamics of precessing black-hole binaries

precession

Author Davide Gerosa

email [e-mail]

Copyright Copyright (C) 2016 Davide Gerosa

Licence CC BY 4.0

Version 1.0.2

DYNAMICS OF SPINNING BLACK-HOLE BINARIES WITH PYTHON

precession is an open-source Python module to study the dynamics of precessing black-hole binaries in the post-Newtonian regime. The code provides a comprehensive toolbox to (i) study the evolution of the black-hole spins along their precession cycles, (ii) perform gravitational-wave driven binary inspirals using both orbit-averaged and precession-averaged integrations, and (iii) predict the properties of the merger remnant through fitting formulae obtained from numerical relativity simulations. precession is a ready-to-use tool to add the black-hole spin dynamics to larger-scale numerical studies such as gravitational-wave parameter estimation codes, population synthesis models to predict gravitational-wave event rates, galaxy merger trees and cosmological simulations of structure formation. precession provides fast and reliable integration methods to propagate statistical samples of black-hole binaries from/to large separations where they form to/from small separations where they become detectable, thus linking gravitational-wave observations of spinning black-hole binaries to their astrophysical formation history. The code is also a useful tool to compute initial parameters for numerical relativity simulations targeting specific precessing systems.

This code is released to the community under the Creative Commons Attribution International license. Essentially, you may use precession as you like but must make reference to our work. When using precession in any published work, please cite the paper describing its implementation:

• PRECESSION: Dynamics of spinning black-hole binaries with python. D. Gerosa, M. Kesden. PRD 93 (2016) 124066. arXiv:1605.01067

precession is an open-source code distributed under git version-control system on

• github.com/dgerosa/precession

API documentation can be generated automatically in html format from the code docstrings using pdoc, and is uplodad to a dedicated branch of the git repository

• dgerosa.github.io/precession

Further information and scientific results are available at:

• www.tapir.caltech.edu/~dgerosa/precession

• www.davidegerosa.com/precession

INSTALLATION

precession works in python 2.x and has been tested on 2.7.10. It can be installed through pip:

pip install precession

Prerequisites are numpy, scipy and parmap, which can be all installed through pip. Information on all code functions are available through Pyhton’s built-in help system

import precession
help(precession.function)

Several tests and tutorial are available in the submodule precession.test. A detailed description of the functionalies of the code is provided in the scientific paper arXiv:1605.01067, where examples are also presented.

RESULTS

precession has been used in the following published papers:

• Gerosa and Sesana. MNRAS 446 (2015) 38-55. arXiv:1405.2072

• Kesden et al. PRL 114 (2015) 081103. arXiv:1411.0674

• Gerosa et al. MNRAS 451 (2015) 3941-3954. arXiv:1503.06807

• Gerosa et al. PRD 92 (2015) 064016. arXiv:1506.03492

• Gerosa et al. PRL 115 (2015) 141102. arXiv:1506.09116

• Trifiro’ et al. PRD 93 (2016) 044071. arXiv:1507.05587

• Gerosa and Kesden. PRD 93 (2016) 124066. arXiv:1605.01067

• Gerosa and Moore. PRL 117 (2016) 011101. arXiv:1606.04226

• Rodriguez et al. APJL 832 (2016) L2 arXiv:1609.05916

• Gerosa et al. CQG 34 (2017) 6, 064004 arXiv:1612.05263

• Gerosa and Berti. PRD 95 (2017) 124046. arXiv:1703.06223

• Zhao et al. PRD 96 (2017) 024007. arXiv:1705.02369

• Wysocki et al. arXiv:1709.01943

RELEASES

v1.0.0 (stable)

CREDITS

The code is developed and maintained by Davide Gerosa. Please, report bugs to

[e-mail]

I am happy to help you out!

Thanks: M. Kesden, U. Sperhake, E. Berti, R. O’Shaughnessy, A. Sesana, D. Trifiro’, A. Klein, J. Vosmera and X. Zhao.