Detecting Features in the Dark Energy Equation of State: A Wavelet Approach

TL;DR

This paper demonstrates that wavelet analysis can effectively detect local features and potential evolution in the dark energy equation of state w(z) from cosmological data, revealing hints of dark energy dynamics.

Contribution

The study introduces a wavelet-based method for identifying features in w(z) and applies it to real and simulated data, showing its effectiveness in detecting dark energy evolution.

Findings

Wavelet analysis successfully detects a bump in simulated data.

Weak hints of dark energy dynamics are found in real data.

Models with w(z) crossing -1 are mildly favored at 95% confidence.

Abstract

We study the utility of wavelets for detecting the redshift evolution of the dark energy equation of state w(z) from the combination of supernovae, CMB and BAO data. We show that local features in w, such as bumps, can be detected efficiently using wavelets. To demonstrate, we first generate a mock supernovae (SNe) data sample for a SNAP-like survey with a bump feature in w(z) hidden in, then successfully discover it by performing a blind wavelet analysis. We also apply our method to analyze the recently released "Constitution" SNe data, combined with WMAP and BAO from SDSS, and find weak hints of dark energy dynamics. Namely, we find that models with w(z) < -1 for 0.2 < z < 0.5, and w(z)> -1 for 0.5 < z <1, are mildly favored at 95% confidence level. This is in good agreement with several recent studies using other methods, such as redshift binning with principal component analysis (PCA) (e.g. Zhao and Zhang, arXiv:0908.1568)