Constraints on Cubic and $f(P)$ Gravity from the Cosmic Chronometers, BAO & CMB datasets : Use of Machine Learning Algorithms

Abstract

In this work, we perform observational data analysis on Einsteinian cubic gravity and $f(P)$ gravity to constrain the parameter space of the theories. We use the 30-point $z-H(z)$ cosmic chronometer data as the observational tool for our analysis along with the BAO and the CMB peak parameters. The ${\chi }^2$ statistic is used for the fitting analysis and it is minimized to obtain the best fit values for the free model parameters. We have used the Markov chain Monte Carlo algorithm to obtain bounds for the free parameters. To achieve this we used the publicly available CosmoMC code to put parameter bounds and subsequently generate contour plots for them with different confidence intervals. Besides finding the Hubble parameter $H$ in terms of the redshift $z$ theoretically from our gravity models, we have exercised correlation coefficients and two machine learning models, namely the linear regression (LR) and artificial neural network (ANN), for the estimation of $H(z)$. For this purpose, we have developed a Python package for finding the parameter space and performing the subsequent statistical analysis and prediction analysis using machine learning. We compared both our theoretical and estimated values of $H(z)$ with the observations. It is seen that our theoretical and estimated models from machine learning performed significantly well when compared with the observations.

20 pages, 7 figures, This version matches the published version with some linguistic changes

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Cited by in corpus (4)

• Tachyonic cosmology with cubic contractions of the Riemann tensor
• Ricci-cubic holographic dark energy
• Anisotropic cosmology using observational datasets: exploring via machine learning approaches
• Ricci-Cubic Holographic Dark Energy: Confronting Observations, Stability and the Cosmic Coincidence Problem