Adding eccentricity to quasicircular binary-black-hole waveform models

TL;DR

This paper introduces a novel method to incorporate eccentricity into existing quasicircular binary-black-hole waveform models, enhancing gravitational-wave data analysis capabilities for eccentric mergers.

Contribution

It presents a new technique to convert circular waveform models into eccentric ones using numerical simulations and interpolation, covering mass ratios up to 3 and eccentricities up to 0.2.

Findings

Minimum overlap with numerical simulations is around 0.98.

The method accurately reconstructs amplitude and phase modulations.

The approach is implemented in an accessible Python package.

Abstract

The detection of gravitational-wave signals from coalescing eccentric binary black holes would yield unprecedented information about the formation and evolution of compact binaries in specific scenarios, such as dynamical formation in dense stellar clusters and three-body interactions. The gravitational-wave searches by the ground-based interferometers, LIGO and Virgo, rely on analytical waveform models for binaries on quasicircular orbits. Eccentric merger waveform models are less developed, and only few numerical simulations of eccentric mergers are publicly available, but several eccentric inspiral models have been developed from the post-Newtonian expansion. Here we present a novel method to convert the dominant quadrupolar mode of any circular analytical binary-black-hole model into an eccentric model. First, using numerical simulations, we examine the additional amplitude and frequency modulations of eccentric signals that are not present in their circular counterparts. Subsequently, we identify suitable analytical descriptions of those modulations and interpolate key parameters from twelve numerical simulations designated as our training dataset. This allows us to reconstruct the modulated amplitude and phase of any waveform up to mass ratio 3 and eccentricity 0.2. We find that the minimum overlap of the new model with numerical simulations is around 0.98 over all of our test dataset that are scaled to a 50M$_\odot$ black-hole binary starting at 35 Hz with aLIGO A+ design sensitivity. A Python package \pyrex easily carries out the computation of this method.