Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

Abstract

We present a new class of AI models for 
the detection of quasi-circular, spinning, 
non-precessing binary black hole mergers 
whose waveforms include the higher order gravitational wave modes $(\ell, |m|)=\{(2, 2), (2, 1), (3, 3), 
(3, 2), (4, 4)\}$, and mode mixing effects 
in the \(\ell = 3, |m| = 2\) harmonics. 
These AI models combine hybrid dilated convolution 
neural networks to accurately model both short- and long-range 
temporal sequential information of gravitational waves; 
and graph neural networks to capture spatial 
correlations among gravitational wave observatories 
to consistently describe and identify the presence of a signal 
in a three detector network encompassing the Advanced 
LIGO and Virgo detectors. We first trained these 
spatiotemporal-graph AI models using synthetic noise, 
using 1.2 million modeled waveforms to densely sample this 
signal manifold, within 1.7 hours using 256 NVIDIA 
A100 GPUs in the Polaris 
supercomputer at the Argonne Leadership Computing 
Facility. This distributed training approach exhibited optimal classification performance, and strong 
scaling up to 512 NVIDIA 
A100 GPUs. With these AI 
ensembles we processed data from a three detector 
network, and found that an 
ensemble of 4 AI models achieves 
state-of-the-art 
performance for signal detection, and reports two 
misclassifications for every decade of 
searched data. We distributed AI inference over 
128 GPUs in the Polaris 
supercomputer and 128 nodes in the Theta supercomputer, 
and completed the processing of a decade of gravitational 
wave data from a three detector network within 3.5 hours. 
Finally, we fine-tuned 
these AI ensembles to 
process the entire month of February 2020, 
which is part of the O3b 
LIGO/Virgo observation run, and found 6 gravitational waves, 
concurrently identified in Advanced LIGO and 
Advanced Virgo data, and zero false positives. 
This analysis was completed in one hour using one 
NVIDIA A100 GPU.