Nature-inspired optimization, the Philippine Eagle, and cosmological parameter estimation

TL;DR

The paper assesses nature-inspired optimization algorithms for cosmological parameter estimation by applying GA, IMODE, and the Philippine Eagle Optimization Algorithm (PEOA) to mock ΛCDM data and comparing results to MCMC. By bootstrapping mock datasets, it evaluates accuracy and precision across methods using observables $H(z)$, $f\sigma_8(z)$, and $\mu(z)$, finding that PEOA achieves performance comparable to MCMC and often surpasses GA and IMODE in precision. The study emphasizes that EAs optimize likelihoods rather than sample posteriors, yet bootstrapped uncertainty enables robust cross-validation of cosmological inferences. The work suggests EAs, particularly PEOA, can complement standard Bayesian approaches, offering alternative pathways to explore high-dimensional parameter spaces with potential for broader cosmological applicability and robustness.

Abstract

Precise and accurate estimation of cosmological parameters is crucial for understanding the Universe's dynamics and addressing cosmological tensions. In this methods paper, we explore bio-inspired metaheuristic algorithms, including the Improved Multi-Operator Differential Evolution scheme and the Philippine Eagle Optimization Algorithm (PEOA), alongside the relatively known genetic algorithm, for cosmological parameter estimation. Using mock data that underlay a true fiducial cosmology, we test the viability of each optimization method to recover the input cosmological parameters with confidence regions generated by bootstrapping on top of optimization. We compare the results with Markov chain Monte Carlo (MCMC) in terms of accuracy and precision, and show that PEOA performs comparably well under the specific circumstances provided. Understandably, Bayesian inference and optimization serve distinct purposes, but comparing them highlights the potential of nature-inspired algorithms in cosmological analysis, offering alternative pathways to explore parameter spaces and validate standard results.

Figures (13)

• Figure 1: Residuals, $\Delta y = y_{\rm mock}-y_{\rm true}$, in one (colored) and a hundred (gray) realisations of mock data of expansion rate, $H(Z)$, growth rate, $f \sigma_8(Z)$, and supernovae distance-modulus, $\mu(Z)$; generation process described in Section \ref{['sec:mock_data']}; horizontal line at $y=0$ is obtained from fiducial $\Lambda$CDM model ($\Omega_{m0}=0.3$, $H_0=70$ km s$^{-1}$Mpc$^{-1}$, $\sigma_{8}=0.8$).
• Figure 2: Flowchart illustrating the key steps in GA.
• Figure 3: Flowchart of IMODE algorithm imodemendoza2022adjusting.
• Figure 4: The three-stage hunting behavior of the Philippine Eagle begins with a preparatory phase, during which it perches and vocalizes. This is followed by the hunting action itself, as the eagle dives from its perch. Finally, it ascends in a circular motion to return to its original position bhlpart209359.
• Figure 5: Flowchart of PEOA Enriquez_Mendoza_Velasco_2022.