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2020

Machine Learning meets the redshift evolution of the CMB Temperature

Arjona, Rubén

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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 $\tau(z)$.

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