Machine Learning meets the redshift evolution of the CMB Temperature

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.

arXiv comment19 pages, 5 figures and 2 tables. Changes match published version. The Genetic Algorithm code can be found at: https://github.com/RubenArjona/Genetic-Algorithms

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