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Title:
The Baryonic Acoustic Feature and Large-Scale Clustering in the SDSS LRG Sample
Authors:
Kazin, Eyal A.; Blanton, Michael R.; Scoccimarro, Roman; McBride, Cameron K.; Berlind, Andreas A.; Bahcall, Neta A.; Brinkmann, Jon; Czarapata, Paul; Frieman, Joshua A.; Kent, Stephan M.; Schneider, Donald P.; Szalay, Alexander S.
Publication:
eprint arXiv:0908.2598
Publication Date:
08/2009
Origin:
ARXIV
Keywords:
Astrophysics - Cosmology and Extragalactic Astrophysics
Comment:
Main text- 19 pages (single column), Abstract- 6 pages, Figures- 19 pages. Submitted to ApJ. Double column version, DATA used and RESULTS may be accessed at: http://cosmo.nyu.edu/~eak306/kazin09a.html
Bibliographic Code:
2009arXiv0908.2598K

Abstract

We examine the correlation function \xi of the Sloan Digital Sky Survey (SDSS) Luminous Red Galaxy sample (LRG) at large scales (60<s<400 Mpc/h) using the final data release (DR7; 105,831 LRGs between 0.16<z<0.47). Using mock catalogs, we demonstrate that the observed baryonic acoustic peak and larger scale signal are consistent with LCDM at the 1.5\sigma level. The signal at 155<s<200 Mpc/h tends to be high relative to theoretical expectations; this slight deviation can be attributed to a bright subsample of the LRGs. Fitting data to a non-linear, redshift-space, template based-model, we constrain the peak position at s_p=103.6+3.6-2.4 Mpc/h when fitting the range 60<s<150 Mpc/h (1\sigma uncertainties measured from the mocks. This redshift-space distance s_p is related to the comoving sound horizon scale r_s after taking into account matter clustering non-linearities, redshift distortions and galaxy clustering bias. Mock catalogs show that the probability that a DR7-sized sample would not have an identifiable peak is at least 10%. As a consistency check of a fiducial cosmology, we use the observed s_p to obtain the distance D_V=[(1+z)^2D_A^2cz/H(z)]^(1/3) relative to the acoustic scale. We find r_s/D_V(z=0.278)=0.1394+-0.0049. This result is in excellent agreement with Percival et. al (2009), who examine roughly the same data set, but using the power spectrum. Comparison with other determinations in the literature are also in very good agreement. We have tested our results against a battery of possible systematic effects, finding all effects are smaller than our estimated sample variance.
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