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Evolutionary optimization of cosmological parameters using metropolis acceptance criterion

Supin P Surendran, Aiswarya A, Rinsy Thomas, Minu Joy

TL;DR

The paper addresses the computational challenge of cosmological parameter inference by introducing an evolutionary optimization framework that integrates a Metropolis-Hastings acceptance criterion. This hybrid method leverages population-based search and parallelism to efficiently constrain ΛCDM parameters using Pantheon, OHD, and Planck data, while enabling direct handling of the $H_0$ tension. Results demonstrate parameter estimates consistent with prior work but achieved with fewer iterations and faster convergence, and reveal a tension between Planck and low-redshift measurements. The approach offers a flexible, scalable alternative or supplement to MCMC for cosmological analyses, with potential to broaden search spaces and improve convergence in high-dimensional settings.

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 $ΛCDM$ 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.

Evolutionary optimization of cosmological parameters using metropolis acceptance criterion

TL;DR

The paper addresses the computational challenge of cosmological parameter inference by introducing an evolutionary optimization framework that integrates a Metropolis-Hastings acceptance criterion. This hybrid method leverages population-based search and parallelism to efficiently constrain ΛCDM parameters using Pantheon, OHD, and Planck data, while enabling direct handling of the tension. Results demonstrate parameter estimates consistent with prior work but achieved with fewer iterations and faster convergence, and reveal a tension between Planck and low-redshift measurements. The approach offers a flexible, scalable alternative or supplement to MCMC for cosmological analyses, with potential to broaden search spaces and improve convergence in high-dimensional settings.

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 cosmological model as well as the uncertainties and identify the discrepancy in the Hubble parameter . 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.
Paper Structure (22 sections, 11 equations, 3 figures, 1 table)

This paper contains 22 sections, 11 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Cosmology, Theory and Observations
  3. Type Ia Supernova observations and inferences
  4. CMB: experiments and inference
  5. Datasets
  6. Pantheon
  7. Observational Hubble parameter Data (OHD)
  8. Cosmic Chronometer (standard clock)
  9. Radial BAO size method (standard ruler)
  10. Gravitational Waves (standard siren)
  11. Planck
  12. Methodology
  13. MCMC based approach
  14. MH Algorithm
  15. Genetic Algorithm
  16. ...and 7 more sections

Figures (3)

  • Figure 1: Analysis for parameters of $\Lambda CDM$ model for Pantheon data from the samples obtained from the iterations of the algorithm.
  • Figure 2: Analysis for parameters of $\Lambda CDM$ model for Observational Hubble parameter data from the samples obtained from the evolutionary algorithm.
  • Figure 3: Analysis for parameters of $\Lambda CDM$ model for Planck data from the chains obtained from the evolutionary algorithm.