Papers › LearningMatch: Siamese Neural Network Learns the Match Manifold
LearningMatch: Siamese Neural Network Learns the Match Manifold
Susanna Green, Andrew Lundgren, Xan Morice-Atkinson
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The match, which is defined as the the similarity between two waveform templates, is a fundamental calculation in computationally expensive gravitational-wave data-analysis pipelines, such as template bank generation. In this paper we introduce LearningMatch, a Siamese neural network that has learned the mapping between the parameters, specifically λ₀ (which is proportional to the chirp mass), η (symmetric mass ratio), and equal aligned spin (χ₁ = χ₂), of two gravitational-wave templates and the match. The trained Siamese neural network, called LearningMatch, can predict the match to within 3.3% of the actual match value. For match values greater than 0.95, a trained LearningMatch model can predict the match to within 1% of the actual match value. LearningMatch can predict the match in 20 μs (mean maximum value) with Graphical Processing Units (GPUs). LearningMatch is 3 orders of magnitudes faster at determining the match than current standard mathematical calculations that involve the template being generated.
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