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