Eccentric, nonspinning, inspiral, Gaussian-process merger approximant for the detection and characterization of eccentric binary black hole mergers
E. A. Huerta, C. J. Moore, Prayush Kumar, Daniel George, Alvin J. K., Chua, Roland Haas, Erik Wessel, Daniel Johnson, Derek Glennon, Adam Rebei, A., Miguel Holgado, Jonathan R. Gair, Harald P. Pfeiffer

TL;DR
The paper introduces ENIGMA, a waveform model for eccentric, non-spinning binary black hole mergers, combining analytical and machine learning techniques, validated against numerical relativity, and useful for gravitational wave data analysis.
Contribution
ENIGMA is the first comprehensive inspiral-merger-ringdown waveform model for eccentric, non-spinning binaries that integrates post-Newtonian, self-force, perturbation theory, and machine learning.
Findings
ENIGMA accurately reproduces quasi-circular binary dynamics.
It can recover eccentricities up to 0.2 at 10Hz.
Eccentricities around 0.1 can cause misclassification as circular.
Abstract
We present , a time domain, inspiral-merger-ringdown waveform model that describes non-spinning binary black holes systems that evolve on moderately eccentric orbits. The inspiral evolution is described using a consistent combination of post-Newtonian theory, self-force and black hole perturbation theory. Assuming 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 reproduces with excellent accuracy the dynamics of quasi-circular compact binaries. We validate using a set of eccentric numerical relativity waveforms, which describe eccentric binary black hole mergers with mass-ratios…
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