Gaussian Process Estimation of Transition Redshift

TL;DR

This study uses Gaussian Process regression on $H(z)$ and SNe Ia data to estimate the cosmic transition redshift $z_t$, providing model-independent constraints on the onset of cosmic acceleration.

Contribution

It introduces a non-parametric Gaussian Process approach to estimate $z_t$ without assuming specific cosmological models, demonstrating consistency across different kernels.

Findings

Estimated $z_t$ from $H(z)$ data: 0.59^{+0.12}_{-0.11}

Estimated $z_t$ from SNe Ia data: 0.683^{+0.11}_{-0.082}

Results are consistent across multiple kernel choices.

Abstract

This paper aims to put constraints on the transition redshift $z_t$, which determines the onset of cosmic acceleration, in cosmological-model independent frameworks. In order to do that, we use the non-parametric Gaussian Process method with $H(z)$ and SNe Ia data. The deceleration parameter reconstruction from $H(z)$ data yields $z_t=0.59^{+0.12}_{-0.11}$. The reconstruction from SNe Ia data assumes spatial flatness and yields $z_t=0.683^{+0.11}_{-0.082}$. These results were found with a Gaussian kernel and we show that they are consistent with two other kernel choices.