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 \texttt{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 AI classifiers and 2 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 Supercomputing 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.

我们介绍了同时处理来自LIGO探测器和Virgo探测器数据的时空图模型。我们使用240万个描述准圆形、旋转、无进动的二进制黑洞合并物的IMRPhenomXPHM波形进行了这些AI分类器的训练，并在联合体中使用了3个AI分类器和2个预测器来处理了一个为期一年的测试集。这些研究表明，我们的AI联合体提供了最先进的信号检测准确性，并报告每年搜索数据的2个错误分类。这是第一个用于搜索和发现高阶引力波模式信号的AI联合体。

用于准圆形，旋转，非进动双黑洞合并的高阶引力波模态信号检测的AI集成