LearningMatch: Siamese Neural Network Learns the Match Manifold
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910810868023296 |
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| author | Green, Susanna Lundgren, Andrew Morice-Atkinson, Xan |
| author_facet | Green, Susanna Lundgren, Andrew Morice-Atkinson, Xan |
| contents | 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 $λ_{0}$ (which is proportional to the chirp mass), $η$ (symmetric mass ratio), and equal aligned spin ($χ_{1}$ = $χ_{2}$), 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 $μ$s (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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01361 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LearningMatch: Siamese Neural Network Learns the Match Manifold Green, Susanna Lundgren, Andrew Morice-Atkinson, Xan General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics 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 $λ_{0}$ (which is proportional to the chirp mass), $η$ (symmetric mass ratio), and equal aligned spin ($χ_{1}$ = $χ_{2}$), 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 $μ$s (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. |
| title | LearningMatch: Siamese Neural Network Learns the Match Manifold |
| topic | General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2502.01361 |