Cosmic chronometers constraints on some fast-varying dark energy equations of state

TL;DR

This study tests fast-varying dark energy models against recent cosmological data, finding current observations insufficient to conclusively support or refute their rapid transition features.

Contribution

It introduces four-parameter dark energy equations of state with rapid transition capabilities and assesses their viability using the latest observational constraints.

Findings

Current data cannot decisively confirm or reject fast-varying dark energy models.

Precise measurement of model parameters is challenging with many free parameters.

Information criteria favor simpler models over fast-varying dark energy scenarios.

Abstract

We consider three `four-parameters' dark energy equations of state allowing fast transition from the matter dominated decelerating phase to the current accelerating phase. The fast-varying nature of the dark energy models is quantified by the transition width $\tau > 0$, a free parameter associated with the models where lower values of $\tau$ imply faster transition. We impose the latest observational constraints on these fast-varying dark energy equations of state, using the latest released cosmic chronometers data along with a series of standard dark energy probes, namely, the local Hubble constant value at 2.4% precision measured by the Hubble Space Telescope, the Joint Light Curve Analysis from Supernovae Type Ia, Baryon acoustic oscillations distance measurements and finally the cosmic microwave background radiation distance priors. Our analyses show that the precise measurements of the free parameters, when a large number of parameters are allowed in a cosmological model become very hard. Moreover, the analyses do not enable us to make any decisive comment on the fast-varying nature of the models, at least from the astronomical data available at current moment. Finally, we close the work with a discussion based on the information criteria, which do not return favorable results to the fast-varying models, at least according to the data employed.