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∊ [0,3] 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 β parameter from the parametrisation T(z)=T0(1+z)1−β, the duality relation η(z) and the cosmic opacity parameter τ(z). Furthermore we place constraints on a temporal varying fine structure constant α, 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 1σ region from the well established ΛCDM model, thus confirming its predictive potential.