  We introduce spatiotemporal-graph models that concurrently process data from
the twin advanced LIGO detectors and the advanced Virgo detector. We trained
these AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describe
quasi-circular, spinning, non-precessing binary black hole mergers with
component 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 using
distributed training over 96 NVIDIA V100 GPUs in the Summit supercomputer. We
then used transfer learning to create AI predictors that estimate the total
mass of potential binary black holes identified by all AI classifiers in the
ensemble. We used this ensemble, 3 classifiers for signal detection and 2 total
mass predictors, to process a year-long test set in which we injected 300,000
signals. This year-long test set was processed within 5.19 minutes using 1024
NVIDIA A100 GPUs in the Polaris supercomputer (for AI inference) and 128 CPU
nodes in the ThetaKNL supercomputer (for post-processing of noise triggers),
housed at the Argonne Leadership Computing Facility. These studies indicate
that our AI ensemble provides state-of-the-art signal detection accuracy, and
reports 2 misclassifications for every year of searched data. This is the first
AI ensemble designed to search for and find higher order gravitational wave
mode signals.
