{"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/mode-by-mode-relative-binning-fast-likelihood","title":"Mode-by-mode Relative Binning: Fast Likelihood Estimation for Gravitational Waveforms with Spin-Orbit Precession and Multiple Harmonics","arxiv_id":"2109.09872","date":"2021-09-20","proceeding":null,"authors":["Nathaniel Leslie","Liang Dai","Geraint Pratten"],"abstract":"Faster likelihood evaluation enhances the efficiency of gravitational wave signal analysis. We present Mode-by-mode Relative Binning (MRB), a new method designed for obtaining fast and accurate likelihoods for advanced waveform models that include spin-orbit precession effects and multiple radiation harmonics from compact binary coalescence. Leveraging the \"twisting-up\" procedure of constructing precessing waveform modes from non-precessing ones, the new method mitigates degrade of relative binning accuracy due to interference from superimposed modes. Additionally, we supplement algorithms for optimizing the choice of frequency bins specific to any given strain signal under analysis. Using the new method, we are able to evaluate the likelihood with up to an order of magnitude reduction in the number of waveform model calls per frequency compared to the previously used relative binning scheme, and achieve better likelihood accuracy than is sufficient for obtaining source parameter posterior distributions that are indistinguishable from the exact ones.","url_abs":"https://arxiv.org/abs/2109.09872v2","url_pdf":"https://arxiv.org/pdf/2109.09872v2.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":"mode-by-mode-relative-binning-fast-likelihood","repo_url":"https://github.com/lemnis12/relativebilbying","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","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}