Thawing k-essence dark energy in the PAge space

TL;DR

This paper investigates thawing k-essence dark energy models within the PAge parameter space, demonstrating that theoretical priors significantly enhance cosmological parameter constraints from future cosmic shear data.

Contribution

It introduces a general framework for analyzing thawing k-essence models in PAge space and quantifies the improvement in parameter estimation when applying the theoretical prior.

Findings

Thawing k-essence models cluster in a narrow band in PAge space.

Theoretical prior improves the figure of merit by a factor of 3.3.

Simulation of cosmic shear data demonstrates the benefit of the prior.

Abstract

A broad class of dark energy models can be written in the form of k-essence, whose Lagrangian density is a two-variable function of a scalar field $\phi$ and its kinetic energy $X\equiv \frac{1}{2}\partial^\mu\phi \partial_\mu\phi$. In the thawing scenario, the scalar field becomes dynamic only when the Hubble friction drops below its mass scale in the late universe. Thawing k-essence dark energy models can be randomly sampled by generating the Taylor expansion coefficients of its Lagrangian density from random matrices \cite{thaws}. Ref. \cite{thaws} points out that the non-uniform distribution of effective equation of state parameters $(w_0, w_a)$ of thawing k-essence model can be used to improve the statistics of model selection. The present work studies the statistics of thawing k-essence in a more general framework that is Parameterized by the Age of the universe (PAge) \cite{PAge}. For fixed matter fraction $\Omega_m$, the random thawing k-essence models cluster in a narrow band in the PAge parameter space, providing a strong theoretical prior. We simulate cosmic shear power spectrum data for the Chinese Space Station Telescope optical survey, and compare the fisher forecast with and without the theoretical prior of thawing k-essence. For an optimal tomography binning scheme, the theoretical prior improves the figure of merit in PAge space by a factor of $3.3$.