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arxiv: 1605.01067 · v3 · submitted 2016-05-03 · 🌌 astro-ph.HE · astro-ph.CO· astro-ph.GA· gr-qc

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PRECESSION: Dynamics of spinning black-hole binaries with python

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classification 🌌 astro-ph.HE astro-ph.COastro-ph.GAgr-qc
keywords black-holeprecessionbinariesgravitational-wavecodedynamicspythonsimulations
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We present the numerical code PRECESSION: a new 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. PRECESSION can be installed from the Python Package Index and it is freely distributed under version control on Github, where further documentation is provided.

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  1. Surrogate models for precessing binary black hole simulations with unequal masses

    gr-qc 2019-05 unverdicted novelty 6.0

    New surrogate models NRSur7dq4 and RemnantModel accurately predict waveforms and remnant properties for precessing unequal-mass binary black holes up to q=4, outperforming existing models by an order of magnitude.