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We determine the mass-ratio, eccentricity and binary inclination angles thatmaximize 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 ofeccentric binary black hole mergers. We carry out this study using numericalrelativity waveforms that describe non-spinning black hole binaries withmass-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, binaryblack hole mergers, and assuming LIGO's Zero Detuned High Power configuration,we find that in regions of parameter space where black hole mergers modeledwith $\ell=|m|=2$ waveforms have vanishing signal-to-noise ratios, theinclusion of $(\ell, \, |m|)$ modes enables the observation of these sourceswith signal-to-noise ratios that range between 30\% to 45\% the signal-to-noiseratio 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, weconstruct waveform signals that describe these astrophysically motivatescenarios, and demonstrate that these topologically complex signals can bedetected and characterized in real LIGO noise with deep learning algorithms.

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