Comprehensive cosmographic analysis by Markov Chain Method
Salvatore Capozziello, Ruth Lazkoz, Vincenzo Salzano

TL;DR
This paper employs a Markov Chain Monte Carlo approach to analyze cosmographic series expansions up to the fourth order, providing insights into the universe's acceleration, jerk, and snap parameters, and their implications for dark energy models.
Contribution
It extends cosmographic analysis to fourth order and demonstrates the effectiveness of MCMC in constraining higher-order cosmographic and dark energy parameters.
Findings
Cosmography confirms current acceleration of the universe.
MCMC yields tighter constraints on jerk and snap parameters.
Results suggest possible deviations from the LCDM model.
Abstract
We study the possibility to extract model independent information about the dynamics of the universe by using Cosmography. We intend to explore it systematically, to learn about its limitations and its real possibilities. Here we are sticking to the series expansion approach on which Cosmography is based. We apply it to different data sets: Supernovae Type Ia (SNeIa), Hubble parameter extracted from differential galaxy ages, Gamma Ray Bursts (GRBs) and the Baryon Acoustic Oscillations (BAO) data. We go beyond past results in the literature extending the series expansion up to the fourth order in the scale factor, which implies the analysis of the deceleration, q_{0}, the jerk, j_{0} and the snap, s_{0}. We use the Markov Chain Monte Carlo Method (MCMC) to analyze the data statistically. We also try to relate direct results from Cosmography to dark energy (DE) dynamical models…
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