Inferring $S_8(z)$ and $\gamma(z)$ with cosmic growth rate measurements using machine learning
Felipe Avila, Armando Bernui, Alexander Bonilla, and Rafael C. Nunes

TL;DR
This paper employs Gaussian Processes regression to reconstruct the evolution of $S_8(z)$, $\sigma_8(z)$, and $\gamma(z)$ from growth rate data, confirming consistency with standard cosmology and addressing tensions in $S_8$ measurements.
Contribution
It introduces a novel Gaussian Process-based method to infer the cosmic evolution of $S_8(z)$ and related parameters, providing model-dependent estimates and reconciling data tensions.
Findings
$S_8(z)$ is compatible with Planck $\Lambda$CDM predictions.
Estimated $\sigma_8(z=0)$ and $S_8(z=0)$ are 0.766 ± 0.116 and 0.732 ± 0.115.
No significant deviations from standard cosmology in $\sigma_8(z)$, $S_8(z)$, and $\gamma(z)$.
Abstract
Measurements of the cosmological parameter provided by cosmic microwave background and large scale structure data reveal some tension between them, suggesting that the clustering features of matter in these early and late cosmological tracers could be different. In this work, we use a supervised learning method designed to solve Bayesian approach to regression, known as Gaussian Processes regression, to quantify the cosmic evolution of up to . For this, we propose a novel approach to find firstly the evolution of the function , then we find the function . As a sub-product we obtain a minimal cosmological model-dependent and estimates. We select independent data measurements of the growth rate and of according to criteria of non-correlated data, then we perform the Gaussian reconstruction of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
