Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers

TL;DR

This paper combines numerical relativity and deep learning to identify higher-order multipole gravitational wave signals from eccentric binary black hole mergers, improving detection capabilities in noisy LIGO data.

Contribution

It introduces a method to determine when higher-order waveform modes are crucial for detection and demonstrates deep learning can effectively identify complex signals in real noise.

Findings

Higher-order modes significantly enhance detection in certain parameter regions.

Deep learning algorithms can detect complex waveforms in real LIGO noise.

Including multipole modes increases the observable volume of eccentric mergers.

Abstract

We determine the mass-ratio, eccentricity and binary inclination angles that maximize the contribution of the higher-order waveform multipoles $(\ell, \, |m|)= \{(2,\,2),\, (2,\,1),\, (3,\,3),\, (3,\,2), \, (3,\,1),\, (4,\,4),\, (4,\,3),\, (4,\,2),\,(4,\,1)\}$ for the gravitational wave detection of eccentric binary black hole mergers. We carry out this study using numerical relativity waveforms that describe non-spinning black hole binaries with mass-ratios $1\leq q \leq 10$, and orbital eccentricities as high as $e_0=0.18$ fifteen cycles before merger. For stellar-mass, asymmetric mass-ratio, binary black hole mergers, and assuming LIGO's Zero Detuned High Power configuration, we find that in regions of parameter space where black hole mergers modeled with $\ell=|m|=2$ waveforms have vanishing signal-to-noise ratios, the inclusion of $(\ell, \, |m|)$ modes enables the observation of these sources with signal-to-noise ratios that range between 30\% to 45\% the signal-to-noise ratio of optimally oriented binary black hole mergers modeled with $\ell=|m|=2$ numerical relativity waveforms. Having determined the parameter space where $(\ell, \, |m|)$ modes are important for gravitational wave detection, we construct waveform signals that describe these astrophysically motivate scenarios, and demonstrate that these topologically complex signals can be detected and characterized in real LIGO noise with deep learning algorithms.