Detecting features in the dark energy equation of state: a wavelet approach

Abstract

We study the utility of wavelets for detecting the redshiftevolution of the dark energy equation of state w(z) from thecombination of supernovae (SNe), CMB and BAO data. We show that localfeatures in w, such as bumps, can be detected efficiently using wavelets. To demonstrate, we first generate a mock supernovae data sample for a SNAP-likesurvey with a bump feature in w(z) hidden in, then successfullydiscover 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, andfind weak hints of dark energy dynamics. Namely, we find that modelswith 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 goodagreement with several recent studies using other methods, such asredshift binning with principal component analysis (PCA) (e.g. Zhaoand Zhang, arXiv: 0908.1568).