Detection and characterization of eccentric compact binary coalescence at the interface of numerical relativity, analytical relativity and machine learning

We present ENIGMA, a time domain, inspiral-merger-ringdown waveform model that describes non-spinning binary black holes systems that evolve on moderately eccentric orbits (https://arxiv.org/abs/1711.06276). The inspiral evolution is described using a consistent combination of post-Newtonian theory, self-force and black hole perturbation theory. Assuming moderately eccentric binaries that circularize prior to coalescence, we smoothly match the eccentric inspiral with a stand-alone, quasi-circular merger, which is constructed using machine learning algorithms that are trained with quasi-circular numerical relativity waveforms. We show that ENIGMA reproduces with excellent accuracy the dynamics of quasi-circular compact binaries, and numerical relativity waveforms that describe eccentric binary black hole mergers with mass-ratios $1< q < 5.5$, and eccentricities $e < 0.2$ ten orbits before merger. We use ENIGMA to show that if the gravitational wave events GW150914, GW151226, GW170104 and GW170814 have eccentricities $e\sim0.1$ at 10 Hz, they can be misclassified as quasi-circular binaries due to parameter space degeneracies between eccentricity and spin corrections.