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We introduce spatiotemporal-graph models that concurrently process data fromthe twin advanced LIGO detectors and the advanced Virgo detector. We trainedthese AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describequasi-circular, spinning, non-precessing binary black hole mergers withcomponent masses $m_{\{1,2\}}\in[3M_\odot, 50 M_\odot]$, and individual spins$s^z_{\{1,2\}}\in[-0.9, 0.9]$; and which include the $(\ell, |m|) = \{(2, 2),(2, 1), (3, 3), (3, 2), (4, 4)\}$ modes, and mode mixing effects in the $\ell =3, |m| = 2$ harmonics. We trained these AI classifiers within 22 hours usingdistributed training over 96 NVIDIA V100 GPUs in the Summit supercomputer. Wethen used transfer learning to create AI predictors that estimate the totalmass of potential binary black holes identified by all AI classifiers in theensemble. We used this ensemble, 3 classifiers for signal detection and 2 totalmass predictors, to process a year-long test set in which we injected 300,000signals. This year-long test set was processed within 5.19 minutes using 1024NVIDIA A100 GPUs in the Polaris supercomputer (for AI inference) and 128 CPUnodes in the ThetaKNL supercomputer (for post-processing of noise triggers),housed at the Argonne Leadership Computing Facility. These studies indicatethat our AI ensemble provides state-of-the-art signal detection accuracy, andreports 2 misclassifications for every year of searched data. This is the firstAI ensemble designed to search for and find higher order gravitational wavemode signals.

AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers