1,539 research outputs found
On Preferred Axes in WMAP Cosmic Microwave Background Data after Subtraction of the Integrated Sachs-Wolfe Effect
There is currently a debate over the existence of claimed statistical
anomalies in the cosmic microwave background (CMB), recently confirmed in
Planck data. Recent work has focussed on methods for measuring statistical
significance, on masks and on secondary anisotropies as potential causes of the
anomalies. We investigate simultaneously the method for accounting for masked
regions and the foreground integrated Sachs-Wolfe (ISW) signal. We search for
trends in different years of WMAP CMB data with different mask treatments. We
reconstruct the ISW field due to the 2 Micron All-Sky Survey (2MASS) and the
NRAO VLA Sky Survey (NVSS) up to l=5, and we focus on the Axis of Evil (AoE)
statistic and even/odd mirror parity, both of which search for preferred axes
in the Universe. We find that removing the ISW reduces the significance of
these anomalies in WMAP data, though this does not exclude the possibility of
exotic physics. In the spirit of reproducible research, all reconstructed maps
and codes will be made available for download at
http://www.cosmostat.org/anomaliesCMB.html.Comment: Figure 1-2 and Tables 1, D.1, D.2 updated. Main conclusions
unchanged. Accepted for publication in A&A. In the spirit of reproducible
research, all statistical and sparse inpainting codes as well as resulting
products which constitute main results of this paper will be made public
here: http://www.cosmostat.org/anomaliesCMB.htm
Polarized wavelets and curvelets on the sphere
The statistics of the temperature anisotropies in the primordial cosmic
microwave background radiation field provide a wealth of information for
cosmology and for estimating cosmological parameters. An even more acute
inference should stem from the study of maps of the polarization state of the
CMB radiation. Measuring the extremely weak CMB polarization signal requires
very sensitive instruments. The full-sky maps of both temperature and
polarization anisotropies of the CMB to be delivered by the upcoming Planck
Surveyor satellite experiment are hence being awaited with excitement.
Multiscale methods, such as isotropic wavelets, steerable wavelets, or
curvelets, have been proposed in the past to analyze the CMB temperature map.
In this paper, we contribute to enlarging the set of available transforms for
polarized data on the sphere. We describe a set of new multiscale
decompositions for polarized data on the sphere, including decimated and
undecimated Q-U or E-B wavelet transforms and Q-U or E-B curvelets. The
proposed transforms are invertible and so allow for applications in data
restoration and denoising.Comment: Accepted. Full paper will figures available at
http://jstarck.free.fr/aa08_pola.pd
Low-l CMB Analysis and Inpainting
Reconstruction of the CMB in the Galactic plane is extremely difficult due to
the dominant foreground emissions such as Dust, Free-Free or Synchrotron. For
cosmological studies, the standard approach consists in masking this area where
the reconstruction is not good enough. This leads to difficulties for the
statistical analysis of the CMB map, especially at very large scales (to study
for e.g., the low quadrupole, ISW, axis of evil, etc). We investigate in this
paper how well some inpainting techniques can recover the low- spherical
harmonic coefficients. We introduce three new inpainting techniques based on
three different kinds of priors: sparsity, energy and isotropy, and we compare
them. We show that two of them, sparsity and energy priors, can lead to
extremely high quality reconstruction, within 1% of the cosmic variance for a
mask with Fsky larger than 80%.Comment: Submitte
3D galaxy clustering with future wide-field surveys: Advantages of a spherical Fourier-Bessel analysis
Upcoming spectroscopic galaxy surveys are extremely promising to help in
addressing the major challenges of cosmology, in particular in understanding
the nature of the dark universe. The strength of these surveys comes from their
unprecedented depth and width. Optimal extraction of their three-dimensional
information is of utmost importance to best constrain the properties of the
dark universe. Although there is theoretical motivation and novel tools to
explore these surveys using the 3D spherical Fourier-Bessel (SFB) power
spectrum of galaxy number counts , most survey
optimisations and forecasts are based on the tomographic spherical harmonics
power spectrum . We performed a new investigation of the
information that can be extracted from the tomographic and 3D SFB techniques by
comparing the forecast cosmological parameter constraints obtained from a
Fisher analysis in the context of planned stage IV wide-field galaxy surveys.
The comparison was made possible by careful and coherent treatment of
non-linear scales in the two analyses. Nuisance parameters related to a scale-
and redshift-dependent galaxy bias were also included for the first time in the
computation of both the 3D SFB and tomographic power spectra. Tomographic and
3D SFB methods can recover similar constraints in the absence of systematics.
However, constraints from the 3D SFB analysis are less sensitive to unavoidable
systematics stemming from a redshift- and scale-dependent galaxy bias. Even for
surveys that are optimised with tomography in mind, a 3D SFB analysis is more
powerful. In addition, for survey optimisation, the figure of merit for the 3D
SFB method increases more rapidly with redshift, especially at higher
redshifts, suggesting that the 3D SFB method should be preferred for designing
and analysing future wide-field spectroscopic surveys.Comment: 12 pages, 6 Figures. Python package for cosmological forecasts
available at https://cosmicpy.github.io . Updated figures. Matches published
versio
True CMB Power Spectrum Estimation
The cosmic microwave background (CMB) power spectrum is a powerful
cosmological probe as it entails almost all the statistical information of the
CMB perturbations. Having access to only one sky, the CMB power spectrum
measured by our experiments is only a realization of the true underlying
angular power spectrum. In this paper we aim to recover the true underlying CMB
power spectrum from the one realization that we have without a need to know the
cosmological parameters. The sparsity of the CMB power spectrum is first
investigated in two dictionaries; Discrete Cosine Transform (DCT) and Wavelet
Transform (WT). The CMB power spectrum can be recovered with only a few
percentage of the coefficients in both of these dictionaries and hence is very
compressible in these dictionaries. We study the performance of these
dictionaries in smoothing a set of simulated power spectra. Based on this, we
develop a technique that estimates the true underlying CMB power spectrum from
data, i.e. without a need to know the cosmological parameters. This smooth
estimated spectrum can be used to simulate CMB maps with similar properties to
the true CMB simulations with the correct cosmological parameters. This allows
us to make Monte Carlo simulations in a given project, without having to know
the cosmological parameters. The developed IDL code, TOUSI, for Theoretical
pOwer spectrUm using Sparse estImation, will be released with the next version
of ISAP
Wavelets, ridgelets and curvelets on the sphere
We present in this paper new multiscale transforms on the sphere, namely the
isotropic undecimated wavelet transform, the pyramidal wavelet transform, the
ridgelet transform and the curvelet transform. All of these transforms can be
inverted i.e. we can exactly reconstruct the original data from its
coefficients in either representation. Several applications are described. We
show how these transforms can be used in denoising and especially in a Combined
Filtering Method, which uses both the wavelet and the curvelet transforms, thus
benefiting from the advantages of both transforms. An application to component
separation from multichannel data mapped to the sphere is also described in
which we take advantage of moving to a wavelet representation.Comment: Accepted for publication in A&A. Manuscript with all figures can be
downloaded at http://jstarck.free.fr/aa_sphere05.pd
Sparsity and morphological diversity for hyperspectral data analysis
Recently morphological diversity and sparsity have
emerged as new and effective sources of diversity for
Blind Source Separation. Based on these new concepts,
novelmethods such as Generalized Morphological Component
Analysis have been put forward. The latter takes
advantage of the very sparse representation of structured
data in large overcomplete dictionaries, to separate
sources based on their morphology. Building on GMCA,
the purpose of this contribution is to describe a new algorithm
for hyperspectral data processing. Large-scale
hyperspectral data refers to collected data that exhibit
sparse spectral signatures in addition to sparse spatial
morphologies, in specified dictionaries of spectral and
spatial waveforms. Numerical experiments are reported
which demonstrate the validity of the proposed extension
for solving source separation problems involving
hyperspectral data
CMB map restoration
Estimating the cosmological microwave background is of utmost importance for
cosmology. However, its estimation from full-sky surveys such as WMAP or more
recently Planck is challenging: CMB maps are generally estimated via the
application of some source separation techniques which never prevent the final
map from being contaminated with noise and foreground residuals. These spurious
contaminations whether noise or foreground residuals are well-known to be a
plague for most cosmologically relevant tests or evaluations; this includes CMB
lensing reconstruction or non-Gaussian signatures search. Noise reduction is
generally performed by applying a simple Wiener filter in spherical harmonics;
however this does not account for the non-stationarity of the noise. Foreground
contamination is usually tackled by masking the most intense residuals detected
in the map, which makes CMB evaluation harder to perform. In this paper, we
introduce a novel noise reduction framework coined LIW-Filtering for Linear
Iterative Wavelet Filtering which is able to account for the noise spatial
variability thanks to a wavelet-based modeling while keeping the highly desired
linearity of the Wiener filter. We further show that the same filtering
technique can effectively perform foreground contamination reduction thus
providing a globally cleaner CMB map. Numerical results on simulated but
realistic Planck data are provided
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