{"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/templategenn-neural-networks-used-to","title":"TemplateGeNN: Neural Networks used to accelerate Gravitational Wave Template Bank Generation","arxiv_id":"2502.15337","date":"2025-02-21","proceeding":null,"authors":["Susanna Green","Andrew Lundgren"],"abstract":"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 $5 M_{\\odot} \\leq \\mathcal{M}_{c} \\leq 20M_{\\odot}$, symmetric mass ratio varied from $0.1 \\leq \\eta \\leq 0.24999$, and equal aligned spin varied from $-0.99 \\leq \\chi_{1,2}\\leq 0.99$) of 31,640 templates in $\\sim 1$ 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 $5 M_{\\odot} \\leq m_{1, 2} \\leq 25 M_{\\odot}$), 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.","url_abs":"https://arxiv.org/abs/2502.15337v1","url_pdf":"https://arxiv.org/pdf/2502.15337v1.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":"templategenn-neural-networks-used-to","repo_url":"https://github.com/SusannaGreen/TemplateGeNN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}