{"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/bayesian-and-frequentist-perspectives-agree","title":"Bayesian and frequentist perspectives agree on dynamical dark energy","arxiv_id":"2506.12004","date":"2025-06-13","proceeding":null,"authors":["Laura Herold","Tanvi Karwal"],"abstract":"Baryon acoustic oscillation data from the Dark Energy Spectroscopic Instrument (DESI) show evidence of a deviation from a cosmological constant $\\Lambda$ within a Bayesian analysis. In this work, we validate that frequentist constraints from profile likelihoods on the Chevallier-Polarski-Linder parameters $w_0$, $w_a$ are in excellent agreement with the Bayesian constraints when combining with Planck cosmic microwave background, Planck and Atacama Cosmology Telescope lensing, and either Pantheon+ or Dark Energy Survey Y5 supernova data. Further, we assess which datasets drive these constraints by considering the contributions to the $\\chi^2$ from the individual datasets. For profile likelihoods of the matter fraction $\\Omega_\\mathrm{m}$, such an investigation shows internal inconsistencies when assuming $\\Lambda$, which are resolved when assuming a $w_0w_a$ dark-energy model. We infer the equations of state $w(z)$ at the pivot redshifts, supporting previous interpretations that current data appears to be more sensitive to the derivative of $w(z)$ rather than a mean offset from $\\Lambda$. Thus our frequentist analysis corroborates previous findings on dynamical DE.","url_abs":"https://arxiv.org/abs/2506.12004v1","url_pdf":"https://arxiv.org/pdf/2506.12004v1.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":"bayesian-and-frequentist-perspectives-agree","repo_url":"https://github.com/LauraHerold/MontePython_desilike","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"bayesian-and-frequentist-perspectives-agree","repo_url":"https://github.com/tkarwal/cosmo_likelihoods","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"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}