472 research outputs found
Revised WMAP constraints on neutrino masses and other extensions of the minimal CDM model
Recently, two issues concerning the three-year WMAP likelihood code were
pointed out. On large angular scales (), a sub-optimal
likelihood approximation resulted in a small power excess. On small angular
scales (), over-subtraction of unresolved point sources produced
a small power deficit. For a minimal six-parameter cosmological model, these
two effects conspired to decrease the value of by . In
this paper, we study the change in preferred parameter ranges for more
extensive cosmological models, including running of , massive neutrinos,
curvature, and the equation of state for dark energy. We also include
large-scale structure and supernova data in our analysis. We find that the
parameter ranges for , and are not much altered by the
modified analysis. For massive neutrinos the upper limit on the sum of the
neutrino masses decreases from eV to eV when using
the modified WMAP code and WMAP data only. We also find that the shift of
to higher values is quite robust to these extensions of the minimal
cosmological model.Comment: 7 pages. Matching version published in Physical Review D. Figures
changed, references added, additional comment
Bayesian analysis of an anisotropic universe model: systematics and polarization
We revisit the anisotropic universe model previously developed by Ackerman,
Carroll and Wise (ACW), and generalize both the theoretical and computational
framework to include polarization and various forms of systematic effects. We
apply our new tools to simulated WMAP data in order to understand the potential
impact of asymmetric beams, noise mis-estimation and potential Zodiacal light
emission. We find that neither has any significant impact on the results. We
next show that the previously reported ACW signal is also present in the 1-year
WMAP temperature sky map presented by Liu & Li, where data cuts are more
aggressive. Finally, we reanalyze the 5-year WMAP data taking into account a
previously neglected (-i)^{l-l'}-term in the signal covariance matrix. We still
find a strong detection of a preferred direction in the temperature map.
Including multipoles up to l=400, the anisotropy amplitude for the W-band is
found to be g = 0.29 +- 0.031, nonzero at 9 sigma. However, the corresponding
preferred direction is also shifted very close to the ecliptic poles at (l,b)=
(96,30), in agreement with the analysis of Hanson & Lewis, indicating that the
signal is aligned along the plane of the solar system. This strongly suggests
that the signal is not of cosmological origin, but most likely is a product of
an unknown systematic effect. Determining the nature of the systematic effect
is of vital importance, as it might affect other cosmological conclusions from
the WMAP experiment. Finally, we provide a forecast for the Planck experiment
including polarization.Comment: 9 pages, 8 figure
Large-Scale Polarized Foreground Component Separation for Planck
We use Bayesian component estimation methods to examine the prospects for
large-scale polarized map and cosmological parameter estimation with simulated
Planck data assuming simplified white noise properties. The sky signal is
parametrized as the sum of the CMB, synchrotron emission, and thermal dust
emission. The synchrotron and dust components are modelled as power-laws, with
a spatially varying spectral index for synchrotron and a uniform index for
dust. Using the Gibbs sampling technique, we estimate the linear polarisation Q
and U posterior amplitudes of the CMB, synchrotron and dust maps as well as the
two spectral indices in ~4 degree pixels. We use the recovered CMB map and its
covariance in an exact pixel likelihood algorithm to estimate the optical depth
to reionization tau, the tensor-to-scalar ratio r, and to construct conditional
likelihood slices for the EE and BB spectra. Given our foreground model, we
find sigma(tau)~0.004 for tau=0.1, sigma(r)~0.03 for a model with r=0.1, and a
95% upper limit of r<0.02 for r=0.0.Comment: 15 pages, 12 figures, submitted to MNRA
Cosmological Parameters from CMB Maps without Likelihood Approximation
We propose an efficient Bayesian MCMC algorithm for estimating cosmological
parameters from CMB data without use of likelihood approximations. It builds on
a previously developed Gibbs sampling framework that allows for exploration of
the joint CMB sky signal and power spectrum posterior, P(s,Cl|d), and addresses
a long-standing problem of efficient parameter estimation simultaneously in
high and low signal-to-noise regimes. To achieve this, our new algorithm
introduces a joint Markov Chain move in which both the signal map and power
spectrum are synchronously modified, by rescaling the map according to the
proposed power spectrum before evaluating the Metropolis-Hastings accept
probability. Such a move was already introduced by Jewell et al. (2009), who
used it to explore low signal-to-noise posteriors. However, they also found
that the same algorithm is inefficient in the high signal-to-noise regime,
since a brute-force rescaling operation does not account for phase information.
This problem is mitigated in the new algorithm by subtracting the Wiener filter
mean field from the proposed map prior to rescaling, leaving high
signal-to-noise information invariant in the joint step, and effectively only
rescaling the low signal-to-noise component. To explore the full posterior, the
new joint move is then interleaved with a standard conditional Gibbs sky map
move. We apply our new algorithm to simplified simulations for which we can
evaluate the exact posterior to study both its accuracy and performance, and
find good agreement with the exact posterior; marginal means agree to less than
0.006 sigma, and standard deviations to better than 3%. The Markov Chain
correlation length is of the same order of magnitude as those obtained by other
standard samplers in the field.Comment: 9 pages, 3 figures, Published in Ap
- …