{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learningmatch-siamese-neural-network-learns","title":"LearningMatch: Siamese Neural Network Learns the Match Manifold","arxiv_id":"2502.01361","date":"2025-02-03","proceeding":null,"authors":["Susanna Green","Andrew Lundgren","Xan Morice-Atkinson"],"abstract":"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 $\\lambda_{0}$ (which is proportional to the chirp mass), $\\eta$ (symmetric mass ratio), and equal aligned spin ($\\chi_{1}$ = $\\chi_{2}$), 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 $\\mu$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.","url_abs":"https://arxiv.org/abs/2502.01361v1","url_pdf":"https://arxiv.org/pdf/2502.01361v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"learningmatch-siamese-neural-network-learns","repo_url":"https://github.com/susannagreen/learningmatch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}