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.
- Source code: github.com/dgerosa/precession.
- Online documentation: dgerosa.github.io/precession.
- Python Package Index: pypi.python.org/pypi/precession
- v1.0 of the code is carefully presented in the scientific paper 1605.01067:
The code is developed and maintained by Davide Gerosa. Please, report bugs to me: email@example.com.
This work is licensed under the CC BY 4.0 licence. Essentially, you can use PRECESSION as you like, but must make reference to our work. If you publish a paper using this code, please drop me an email, so that it can be included in this webpage.
Thanks: E. Berti, M. Kesden, U. Sperhake, R. O’Shaughnessy, D. Trifiro’, A. Klein, J. Vosmera and X. Zhao.
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
- Trifirò 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
PRECESSION is uploaded in the Python Package Index PyPI. To install the code from PyPI type
pip install precession
To upgrade from a previous version use
pip install -U precession
The code has been tested on python 2.7 and currently is not compatible with python 3. The python libraries numpy, scipy, matplotlib and parmap are specified as essential prerequisites. They can all be installed using pip. If these packages are missing in your system, pip will try to install them when installing PRECESSION. Packages such as scipy are far more complex than PRECESSION: if the installation fail, please refer to their webpage.
To start using the code, enter a python console and type
The submodule precession.test provides examples and introductory tutorial to start using the code.
A minimal working example, where a single PN inspiral is performed, can be executed typing
Various other tests are described in the paper above.
A detailed API documentation is regularly uploaded to a dedicated branch of the git repository and is available online. The python built-in help function also provides information on the module and its functions.