{"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/machine-learning-meets-the-redshift-evolution","title":"Machine Learning meets the redshift evolution of the CMB Temperature","arxiv_id":"2002.12700","date":"2020-02-28","proceeding":null,"authors":["Rubén Arjona"],"abstract":"We present a model independent and non-parametric reconstruction with a Machine Learning algorithm of the redshift evolution of the Cosmic Microwave Background (CMB) temperature from a wide redshift range $z\\in \\left[0,3\\right]$ without assuming any dark energy model, an adiabatic universe or photon number conservation. In particular we use the genetic algorithms which avoid the dependency on an initial prior or a cosmological fiducial model. Through our reconstruction we constrain new physics at late times. We provide novel and updated estimates on the $\\beta$ parameter from the parametrisation $\\text{T}(z)=\\text{T}_0(1+z)^{1-\\beta}$, the duality relation $\\eta(z)$ and the cosmic opacity parameter $\\tau(z)$. Furthermore we place constraints on a temporal varying fine structure constant $\\alpha$, which would have signatures in a broad spectrum of physical phenomena such as the CMB anisotropies. Overall we find no evidence of deviations within the $1\\sigma$ region from the well established $\\Lambda\\text{CDM}$ model, thus confirming its predictive potential.","url_abs":"https://arxiv.org/abs/2002.12700v1","url_pdf":"https://arxiv.org/pdf/2002.12700v1.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":"machine-learning-meets-the-redshift-evolution","repo_url":"https://github.com/DivyanshK12/SymbolicRegression","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"machine-learning-meets-the-redshift-evolution","repo_url":"https://github.com/dkarmy12/GeneticAlgorithmsPractice","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"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}