Evolutionary optimization of cosmological parameters using metropolis acceptance criterion

TL;DR

This paper introduces a parallel evolutionary algorithm for constraining cosmological parameters, demonstrating its effectiveness on multiple datasets with improved computational efficiency and consistent results with existing studies.

Contribution

A new parallel evolutionary optimization method for cosmological parameters that enhances computational efficiency and accuracy over existing techniques.

Findings

Successfully constrained $ m{ extLambda CDM}$ parameters.

Achieved higher computational performance in fewer iterations.

Results are consistent with previous cosmological measurements.

Abstract

A novel evolutionary method is introduced that can be used for constraining the parameters and theoretical models of Cosmology. The newly proposed algorithm, which is inherently parallel by design, is able to obtain the full potential of multi-core machines. With this algorithm, we could obtain the best-fit parameters of the $\Lambda CDM$ cosmological model as well as the uncertainties and identify the discrepancy in the Hubble parameter $H_0$. In the present work we discuss the design principle of this novel approach and also the results from the analysis of Pantheon, OHD and Planck datasets are reported here. The estimation of parameters shows significant consistency with the previously reported values as well as a higher computational performance measured in number iterations compared to the other similar exercises.