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

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 (SNe), 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 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)

Authors:
; ;  [1]
  1. Department of Physics, Simon Fraser University, Burnaby, BC, V5A 1S6 (Canada)
Publication Date:
OSTI Identifier:
22272844
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Cosmology and Astroparticle Physics; Journal Volume: 2010; Journal Issue: 04; Other Information: Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; COSMOLOGICAL MODELS; COSMOLOGY; EQUATIONS OF STATE; NONLUMINOUS MATTER; OSCILLATIONS; RED SHIFT; RELICT RADIATION; SUPERNOVAE

Citation Formats

Hojjati, Alireza, Pogosian, Levon, and Zhao, Gong-Bo, E-mail: alireza_hojjati@sfu.ca, E-mail: levon@sfu.ca, E-mail: gong-bo.zhao@port.ac.uk. Detecting features in the dark energy equation of state: a wavelet approach. United States: N. p., 2010. Web. doi:10.1088/1475-7516/2010/04/007.
Hojjati, Alireza, Pogosian, Levon, & Zhao, Gong-Bo, E-mail: alireza_hojjati@sfu.ca, E-mail: levon@sfu.ca, E-mail: gong-bo.zhao@port.ac.uk. Detecting features in the dark energy equation of state: a wavelet approach. United States. doi:10.1088/1475-7516/2010/04/007.
Hojjati, Alireza, Pogosian, Levon, and Zhao, Gong-Bo, E-mail: alireza_hojjati@sfu.ca, E-mail: levon@sfu.ca, E-mail: gong-bo.zhao@port.ac.uk. Thu . "Detecting features in the dark energy equation of state: a wavelet approach". United States. doi:10.1088/1475-7516/2010/04/007.
@article{osti_22272844,
title = {Detecting features in the dark energy equation of state: a wavelet approach},
author = {Hojjati, Alireza and Pogosian, Levon and Zhao, Gong-Bo, E-mail: alireza_hojjati@sfu.ca, E-mail: levon@sfu.ca, E-mail: gong-bo.zhao@port.ac.uk},
abstractNote = {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 (SNe), 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 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)},
doi = {10.1088/1475-7516/2010/04/007},
journal = {Journal of Cosmology and Astroparticle Physics},
number = 04,
volume = 2010,
place = {United States},
year = {Thu Apr 01 00:00:00 EDT 2010},
month = {Thu Apr 01 00:00:00 EDT 2010}
}