Papers › TemplateGeNN: Neural Networks used to accelerate Gravitational Wave Template Bank Generation
TemplateGeNN: Neural Networks used to accelerate Gravitational Wave Template Bank Generation
Susanna Green, Andrew Lundgren
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We introduce TemplateGeNN, a fast stochastic template bank generation algorithm which uses Graphical Processing Units (GPUs) and a LearningMatch model (Siamese neural network). TemplateGeNN generated a binary black hole template bank (chirp mass varied from 5 M_⊙ ≤ℳ_c ≤20M_⊙, symmetric mass ratio varied from 0.1 ≤η≤0.24999, and equal aligned spin varied from -0.99 ≤χ_(1,2)≤0.99) of 31,640 templates in ∼1 day on a single A100 GPU. To test the sensitivity of this template bank we injected 7746 binary black hole templates into LIGO Gaussian noise. This template bank recovered 98% of the injections with a fitting factor greater than 0.97. For lower mass regions (black hole mass region between 5 M_⊙ ≤m_(1, 2) ≤25 M_⊙), 99% of 9469 injections were recovered with a fitting factor greater than 0.97. LearningMatch and TemplateGeNN are a machine-learning pipeline that can be used to accelerate template bank generation for future gravitational-wave data analysis.
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