908 research outputs found
Semi-blind Bayesian inference of CMB map and power spectrum
We present a new blind formulation of the Cosmic Microwave Background (CMB)
inference problem. The approach relies on a phenomenological model of the
multi-frequency microwave sky without the need for physical models of the
individual components. For all-sky and high resolution data, it unifies parts
of the analysis that have previously been treated separately, such as component
separation and power spectrum inference. We describe an efficient sampling
scheme that fully explores the component separation uncertainties on the
inferred CMB products such as maps and/or power spectra. External information
about individual components can be incorporated as a prior giving a flexible
way to progressively and continuously introduce physical component separation
from a maximally blind approach. We connect our Bayesian formalism to existing
approaches such as Commander, SMICA and ILC, and discuss possible future
extensions.Comment: 11 pages, 9 figure
Full-sky CMB lensing reconstruction in presence of sky-cuts
We consider the reconstruction of the CMB lensing potential and its power
spectrum of the full sphere in presence of sky-cuts due to point sources and
Galactic contaminations. Those two effects are treated separately. Small
regions contaminated by point sources are filled in using Gaussian constrained
realizations. The Galactic plane is simply masked using an apodized mask before
lensing reconstruction. This algorithm recovers the power spectrum of the
lensing potential with no significant bias.Comment: Submitted to A&
Practical wavelet design on the sphere
We address the question of designing isotropic analysis functions on the
sphere which are perfectly limited in the spectral domain and optimally
localized in the spatial domain. This work is motivated by the need of
localized analysis tools in domains where the data is lying on the sphere,
e.g.{} the science of the Cosmic Microwave Background. Our construction is
derived from the localized frames introduced by Narcowich, Petrushev, Ward,
2006. The analysis frames are optimized for given applications and compared
numerically using various criteria
Foreground component separation with generalised ILC
The 'Internal Linear Combination' (ILC) component separation method has been
extensively used to extract a single component, the CMB, from the WMAP
multifrequency data. We generalise the ILC approach for separating other
millimetre astrophysical emissions. We construct in particular a
multidimensional ILC filter, which can be used, for instance, to estimate the
diffuse emission of a complex component originating from multiple correlated
emissions, such as the total emission of the Galactic interstellar medium. The
performance of such generalised ILC methods, implemented on a needlet frame, is
tested on simulations of Planck mission observations, for which we successfully
reconstruct a low noise estimate of emission from astrophysical foregrounds
with vanishing CMB and SZ contamination.Comment: 11 pages, 6 figures (2 figures added), 1 reference added,
introduction expanded, V2: version accepted by MNRA
CMB Polarization can constrain cosmology better than CMB temperature
We demonstrate that for a cosmic variance limited experiment, CMB E
polarization alone places stronger constraints on cosmological parameters than
CMB temperature. For example, we show that EE can constrain parameters better
than TT by up to a factor 2.8 when a multipole range of l=30-2500 is
considered. We expose the physical effects at play behind this remarkable
result and study how it depends on the multipole range included in the
analysis. In most relevant cases, TE or EE surpass the TT based cosmological
constraints. This result is important as the small scale astrophysical
foregrounds are expected to have a much reduced impact on polarization, thus
opening the possibility of building cleaner and more stringent constraints of
the LCDM model. This is relevant specially for proposed future CMB satellite
missions, such as CORE or PRISM, that are designed to be cosmic variance
limited in polarization till very large multipoles. We perform the same
analysis for a Planck-like experiment, and conclude that even in this case TE
alone should determine the constraint on better than TT by 15%,
while determining , and with comparable accuracy.
Finally, we explore a few classical extensions of the LCDM model and show again
that CMB polarization alone provides more stringent constraints than CMB
temperature in case of a cosmic variance limited experiment.Comment: 14 pages, 16 figure
CMB and SZ effect separation with Constrained Internal Linear Combinations
The `Internal Linear Combination' (ILC) component separation method has been
extensively used on the data of the WMAP space mission, to extract a single
component, the CMB, from the WMAP multifrequency data. We extend the ILC
approach for reconstructing millimeter astrophysical emissions beyond the CMB
alone. In particular, we construct a Constrained ILC to extract clean maps of
both the CMB or the thermal Sunyaev Zeldovich (SZ) effect, with vanishing
contamination from the other. The performance of the Constrained ILC is tested
on simulations of Planck mission observations, for which we successfully
reconstruct independent estimates of the CMB and of the thermal SZ.Comment: 7 pages, 3 figures, submitted to MNRA
Bayesian model comparison in cosmology with Population Monte Carlo
We use Bayesian model selection techniques to test extensions of the standard
flat LambdaCDM paradigm. Dark-energy and curvature scenarios, and primordial
perturbation models are considered. To that end, we calculate the Bayesian
evidence in favour of each model using Population Monte Carlo (PMC), a new
adaptive sampling technique which was recently applied in a cosmological
context. The Bayesian evidence is immediately available from the PMC sample
used for parameter estimation without further computational effort, and it
comes with an associated error evaluation. Besides, it provides an unbiased
estimator of the evidence after any fixed number of iterations and it is
naturally parallelizable, in contrast with MCMC and nested sampling methods. By
comparison with analytical predictions for simulated data, we show that our
results obtained with PMC are reliable and robust. The variability in the
evidence evaluation and the stability for various cases are estimated both from
simulations and from data. For the cases we consider, the log-evidence is
calculated with a precision of better than 0.08.
Using a combined set of recent CMB, SNIa and BAO data, we find inconclusive
evidence between flat LambdaCDM and simple dark-energy models. A curved
Universe is moderately to strongly disfavoured with respect to a flat
cosmology. Using physically well-motivated priors within the slow-roll
approximation of inflation, we find a weak preference for a running spectral
index. A Harrison-Zel'dovich spectrum is weakly disfavoured. With the current
data, tensor modes are not detected; the large prior volume on the
tensor-to-scalar ratio r results in moderate evidence in favour of r=0.
[Abridged]Comment: 11 pages, 6 figures. Matches version accepted for publication by
MNRA
Estimation of cosmological parameters using adaptive importance sampling
We present a Bayesian sampling algorithm called adaptive importance sampling
or Population Monte Carlo (PMC), whose computational workload is easily
parallelizable and thus has the potential to considerably reduce the wall-clock
time required for sampling, along with providing other benefits. To assess the
performance of the approach for cosmological problems, we use simulated and
actual data consisting of CMB anisotropies, supernovae of type Ia, and weak
cosmological lensing, and provide a comparison of results to those obtained
using state-of-the-art Markov Chain Monte Carlo (MCMC). For both types of data
sets, we find comparable parameter estimates for PMC and MCMC, with the
advantage of a significantly lower computational time for PMC. In the case of
WMAP5 data, for example, the wall-clock time reduces from several days for MCMC
to a few hours using PMC on a cluster of processors. Other benefits of the PMC
approach, along with potential difficulties in using the approach, are analysed
and discussed.Comment: 17 pages, 11 figure
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