Observational Constraints and Geometric Diagnostics of Barboza-Alcaniz and Logarithmic Dark Energy Parametrizations
Archana Dixit, Saurabh Verma, Anirudh Pradhan, M. S. Barak

TL;DR
This paper compares two dark energy models, Barboza-Alcaniz and Logarithmic, using diverse observational data and statistical methods to assess their viability and differences in cosmic evolution.
Contribution
It provides a detailed comparison of two dark energy parametrizations with observational data, employing MCMC constraints and geometric diagnostics to evaluate their cosmological implications.
Findings
Both models are consistent with current data.
Logarithmic parametrization shows slightly better constraints.
Statefinder analysis distinguishes the models effectively.
Abstract
This study investigates and compares two prominent two-dimensional dark energy (DE) parameterizations: Barboza-Alcaniz (BA) and Logarithmic forms by comparing them with a comprehensive set of observational data comprising Type Ia Supernovae (SNe Ia) from the Pantheon compilation, Baryon Acoustic Oscillations (DESI BAO), and Cosmic Chronometers (CC). The primary objective was to explore the constraining power and cosmological implications of each parameterization in light of the current data. After formulating the theoretical framework and background equations governing cosmic expansion, we employ Markov Chain Monte Carlo (MCMC) techniques using the emcee Python package to constrain the free parameters of each model. The best-fit values for parameters , , and were extracted for each model using individual and combined datasets. The results include confidence…
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Taxonomy
TopicsCosmology and Gravitation Theories · Gamma-ray bursts and supernovae · Galaxies: Formation, Evolution, Phenomena
