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Planck intermediate results. LI. Features in the cosmic microwave background temperature power spectrum and shifts in cosmological parameters

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 Added by Marius Millea
 Publication date 2016
  fields Physics
and research's language is English




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The six parameters of the standard $Lambda$CDM model have best-fit values derived from the Planck temperature power spectrum that are shifted somewhat from the best-fit values derived from WMAP data. These shifts are driven by features in the Planck temperature power spectrum at angular scales that had never before been measured to cosmic-variance level precision. We investigate these shifts to determine whether they are within the range of expectation and to understand their origin in the data. Taking our parameter set to be the optical depth of the reionized intergalactic medium $tau$, the baryon density $omega_{rm b}$, the matter density $omega_{rm m}$, the angular size of the sound horizon $theta_*$, the spectral index of the primordial power spectrum, $n_{rm s}$, and $A_{rm s}e^{-2tau}$ (where $A_{rm s}$ is the amplitude of the primordial power spectrum), we examine the change in best-fit values between a WMAP-like large angular-scale data set (with multipole moment $ell<800$ in the Planck temperature power spectrum) and an all angular-scale data set ($ell<2500$ Planck temperature power spectrum), each with a prior on $tau$ of $0.07pm0.02$. We find that the shifts, in units of the 1$sigma$ expected dispersion for each parameter, are ${Delta tau, Delta A_{rm s} e^{-2tau}, Delta n_{rm s}, Delta omega_{rm m}, Delta omega_{rm b}, Delta theta_*} = {-1.7, -2.2, 1.2, -2.0, 1.1, 0.9}$, with a $chi^2$ value of 8.0. We find that this $chi^2$ value is exceeded in 15% of our simulated data sets, and that a parameter deviates by more than 2.2$sigma$ in 9% of simulated data sets, meaning that the shifts are not unusually large. Comparing $ell<800$ instead to $ell>800$, or splitting at a different multipole, yields similar results. We examine the $ell<800$ model residuals in the $ell>800$ power spectrum data and find that the features there... [abridged]



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