Kernel dependence of the Gaussian Process reconstruction of late Universe expansion history

Understand

In this work, we discuss model-independent reconstruction of the expansion history of the late Universe.

• We use Gaussian Process Regression (GPR) to reconstruct the evolution of various cosmological parameters such as Hubble parameter $H(z)$ and deceleration parameter $q(z)$ using observational data to train the GPR model.
• We look at the GP reconstruction of these parameters using stationary and non-stationary kernel functions.
• We examine the effect of the choice of kernel functions on the reconstructions.