{"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/accelerating-bao-scale-fitting-using-taylor","title":"Accelerating BAO Scale Fitting Using Taylor Series","arxiv_id":"2112.06438","date":"2021-12-13","proceeding":null,"authors":["Matthew Hansen","Alex Krolewski","Zachary Slepian"],"abstract":"The Universe is currently undergoing accelerated expansion driven by dark energy. Dark energy's essential nature remains mysterious: one means of revealing it is by measuring the Universe's size at different redshifts. This may be done using the Baryon Acoustic Oscillation (BAO) feature, a standard ruler in the galaxy 2-Point Correlation Function (2PCF). In order to measure the distance scale, one dilates and contracts a template for the 2PCF in a fiducial cosmology, using a scaling factor $\\alpha$. The standard method for finding the best-fit $\\alpha$ is to compute the likelihood over a grid of roughly 100 values of it. This approach is slow; in this work, we propose a significantly faster way. Our method writes the 2PCF as a polynomial in $\\alpha$ by Taylor-expanding it about $\\alpha = 1$, exploiting that we know the fiducial cosmology sufficiently well that $\\alpha$ is within a few percent of unity. The likelihood resulting from this expansion may then be analytically solved for the best-fit $\\alpha$. Our method is 48-85$\\times$ faster than a directly comparable approach in which we numerically minimize $\\alpha$, and $\\sim$$12,000 \\times$ faster than the standard iterative method. Our work will be highly enabling for upcoming large-scale structure redshift surveys such as that by Dark Energy Spectroscopic Instrument (DESI).","url_abs":"https://arxiv.org/abs/2112.06438v2","url_pdf":"https://arxiv.org/pdf/2112.06438v2.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":"accelerating-bao-scale-fitting-using-taylor","repo_url":"https://github.com/matthew3hansen/bao_scaling_taylor_series","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","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}