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Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers by Tian, Minyang; Huerta, E. A.; Zheng, Huihuo; Kumar, Prayush is a scholarly article available to read on EtoBox.

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

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 $(l, |m|)=\{(2, 2), (2, 1), (3, 3), (3, 2), (4, 4)\}$, and mode mixing effects in the $l = 3, |m| = 2$ 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 A100 GPUs in the Polaris supercomputer at the ALCF. Our distributed training approach had optimal performance, and strong scaling up to 512 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 fo

Author

Tian, Minyang; Huerta, E. A.; Zheng, Huihuo; Kumar, Prayush

Published

2023

Language

EN