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

We determine the mass ratio, eccentricity and binary inclination angles that maximize the contribution of the higher -order waveform multipoles (l, vertical bar m vertical bar) = {(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 nonspinning black hole binaries with mass ratios 1 <= q <= 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 l = m = 2 waveforms have vanishing signal-to-noise ratios, the inclusion of (l, vertical bar m vertical bar) modes enables the observation of these sources with signal-to-noise ratios that range between 30% and 45% of the signal-tonoise ratio of optimally oriented binary black hole mergers modeled with l = vertical bar m vertical bar = 2 numerical relativity waveforms. Having determined the parameter space where (l, vertical bar m vertical bar) modes are important for gravitational wave detection, we construct waveform signals that describe these astrophysically motivated scenarios and demonstrate that these topologically complex signals can be detected and characterized in real LIGO noise with deep learning algorithms