Dynamics of precessing black-hole binaries
Author Davide Gerosa
Copyright Copyright (C) 2016 Davide Gerosa
Licence CC BY 4.0
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
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
Further information and scientific results are available at:
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.
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. PRD 97 (2018) 043014 arXiv:1709.01943
- Gerosa J.Phys.Conf.Ser. 957 (2018) 012014. arXiv:1711.1003
- Rodriguez et al. PRL 120 (2018) 151101. arXiv:1712.0493
- Gerosa et al. PRD 97 (2018) 104049. arXiv:1802.04276
- Gerosa et al. PRD 98 (2018) 084036. arXiv:1808.02491
- Varma et al. arXiv:1809.09125
- Tso et al. arXiv:1807.00075
v1.0.0 Stable version released together with the first arxiv submission of arXiv:1605.01067.
v1.0.2 Clarifications on typos in Eq. (36) and (37) of arXiv:1605.01067. See help(precession) for more information.
v1.0.3 Python 3 now supported (hurray!). By default, finalspin now returns more updated result by Hofmann, Barausse and Rezzolla 2016.
The code is developed and maintained by Davide Gerosa. Please, report bugs to
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.