A New Approach to Testing Dark Energy Models by Observations

TL;DR

This paper introduces a novel observational consistency test for dark energy models, using a characteristic function Q(z) to compare model predictions with data, demonstrated on quintessence potentials.

Contribution

It proposes a new, efficient method to test and constrain dark energy models by reconstructing a characteristic function from observational data.

Findings

The method successfully tests the exponential and power-law quintessence models.

It constrains model parameters using reconstructed Q(z) from data.

The approach is adaptable to other cosmological and scientific models.

Abstract

We propose a new approach to the consistency test of dark energy models with observations. To test a category of dark energy models, we suggest introducing a characteristic Q(z) that in general varies with the redshift z but in those models plays the role of a (constant) distinct parameter. Then, by reconstructing dQ(z)/dz from observational data and comparing it with zero we can assess the consistency between data and the models under consideration. For a category of models that passes the test, we can further constrain the distinct parameter of those models by reconstructing Q(z) from data. For demonstration, in this paper we concentrate on quintessence. In particular we examine the exponential potential and the power-law potential via a widely used parametrization of the dark energy equation of state, w(z) = w_0 + w_a z/(1+z), for data analysis. This method of the consistency test is particularly efficient because for all models we invoke the constraint of only a single parameter space that by choice can be easily accessed. The general principle of our approach is not limited to dark energy. It may also be applied to the testing of various cosmological models and even the models in other fields beyond the scope of cosmology.