Late Time-Transition Redshift as a Cosmic Parameter for the Accelerated Expansion of the Universe

Using a joint statistical analysis, we test a five-dimensional embedded model based on the Nash-Greene embedding theorem at late-time transition redshift. Performing a Markov Chain Monte Carlo (MCMC) modelling, we combine observational data sets as those of the recent Pantheon type Ia supernovae, Baryon Acoustic Oscillations (BAO) and the angular acoustic scale of the Cosmic Microwave Background (CMB) to impose restrictions on the model and correlating the model parameters to mimicking an equation of state. From statistical classifiers as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), we use the Jeffreys' scale and find a strong evidence favoring a statistically consistence a dynamical Dark energy (CPL parameterization) and a relative consistence with the $\Lambda$CDM model and $w$CDM model. Moreover, we find that the transition redshift used as a cosmic discriminator with the best fit $z_t=1.53\pm 0.17$ at 1-$\sigma$ C.L. with a range scenario for sharp late transitions.