80 research outputs found
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
PRISM: Sparse Recovery of the Primordial Power Spectrum
The primordial power spectrum describes the initial perturbations in the
Universe which eventually grew into the large-scale structure we observe today,
and thereby provides an indirect probe of inflation or other
structure-formation mechanisms. Here, we introduce a new method to estimate
this spectrum from the empirical power spectrum of cosmic microwave background
(CMB) maps.
A sparsity-based linear inversion method, coined \textbf{PRISM}, is
presented. This technique leverages a sparsity prior on features in the
primordial power spectrum in a wavelet basis to regularise the inverse problem.
This non-parametric approach does not assume a strong prior on the shape of the
primordial power spectrum, yet is able to correctly reconstruct its global
shape as well as localised features. These advantages make this method robust
for detecting deviations from the currently favoured scale-invariant spectrum.
We investigate the strength of this method on a set of WMAP 9-year simulated
data for three types of primordial power spectra: a nearly scale-invariant
spectrum, a spectrum with a small running of the spectral index, and a spectrum
with a localised feature. This technique proves to easily detect deviations
from a pure scale-invariant power spectrum and is suitable for distinguishing
between simple models of the inflation. We process the WMAP 9-year data and
find no significant departure from a nearly scale-invariant power spectrum with
the spectral index .
A high resolution primordial power spectrum can be reconstructed with this
technique, where any strong local deviations or small global deviations from a
pure scale-invariant spectrum can easily be detected
Likelihood-free inference with neural compression of DES SV weak lensing map statistics
In many cosmological inference problems, the likelihood (the probability of the observed data as a function of the unknown parameters) is unknown or intractable. This necessitates approximations and assumptions, which can lead to incorrect inference of cosmological parameters, including the nature of dark matter and dark energy, or create artificial model tensions. Likelihood-free inference covers a novel family of methods to rigorously estimate posterior distributions of parameters using forward modelling of mock data. We present likelihood-free cosmological parameter inference using weak lensing maps from the Dark Energy Survey (DES) Science Verification data, using neural data compression of weak lensing map summary statistics. We explore combinations of the power spectra, peak counts, and neural compressed summaries of the lensing mass map using deep convolution neural networks. We demonstrate methods to validate the inference process, for both the data modelling and the probability density estimation steps. Likelihood-free inference provides a robust and scalable alternative for rigorous large-scale cosmological inference with galaxy survey data (for DES, Euclid, and LSST). We have made our simulated lensing maps publicly available
Spherical 3D Isotropic Wavelets
Future cosmological surveys will provide 3D large scale structure maps with
large sky coverage, for which a 3D Spherical Fourier-Bessel (SFB) analysis in
spherical coordinates is natural. Wavelets are particularly well-suited to the
analysis and denoising of cosmological data, but a spherical 3D isotropic
wavelet transform does not currently exist to analyse spherical 3D data. The
aim of this paper is to present a new formalism for a spherical 3D isotropic
wavelet, i.e. one based on the SFB decomposition of a 3D field and accompany
the formalism with a public code to perform wavelet transforms. We describe a
new 3D isotropic spherical wavelet decomposition based on the undecimated
wavelet transform (UWT) described in Starck et al. 2006. We also present a new
fast Discrete Spherical Fourier-Bessel Transform (DSFBT) based on both a
discrete Bessel Transform and the HEALPIX angular pixelisation scheme. We test
the 3D wavelet transform and as a toy-application, apply a denoising algorithm
in wavelet space to the Virgo large box cosmological simulations and find we
can successfully remove noise without much loss to the large scale structure.
We have described a new spherical 3D isotropic wavelet transform, ideally
suited to analyse and denoise future 3D spherical cosmological surveys, which
uses a novel Discrete Spherical Fourier-Bessel Transform. We illustrate its
potential use for denoising using a toy model. All the algorithms presented in
this paper are available for download as a public code called MRS3D at
http://jstarck.free.fr/mrs3d.htmlComment: 9 pages + appendices. Public code can be downloaded at
http://jstarck.free.fr/mrs3d.html Corrected typos and updated references.
Accepted for publication in Astronomy and Astrophysic
PRISM: Recovery of the primordial spectrum from Planck data
The primordial power spectrum describes the initial perturbations that seeded
the large-scale structure we observe today. It provides an indirect probe of
inflation or other structure-formation mechanisms. In this letter, we recover
the primordial power spectrum from the Planck PR1 dataset, using our recently
published algorithm PRISM. PRISM is a sparsity-based inversion method, that
aims at recovering features in the primordial power spectrum from the empirical
power spectrum of the cosmic microwave background (CMB). This ill-posed inverse
problem is regularised using a sparsity prior on features in the primordial
power spectrum in a wavelet dictionary. Although this non-parametric method
does not assume a strong prior on the shape of the primordial power spectrum,
it is able to recover both its general shape and localised features. As a
results, this approach presents a reliable way of detecting deviations from the
currently favoured scale-invariant spectrum. We applied PRISM to 100 simulated
Planck data to investigate its performance on Planck-like data. We also tested
the algorithm's ability to recover a small localised feature at
Mpc, which caused a large dip at in the angular power
spectrum. We then applied PRISM to the Planck PR1 power spectrum to recover the
primordial power spectrum. We find no significant departures from the fiducial
Planck PR1 near scale-invariant primordial power spectrum with
and .Comment: 4 pages, 2 figures, Accepted in A&A; Updated to match the final
accepted versio
Spherical 3D Isotropic Wavelets
Future cosmological surveys will provide 3D large scale structure maps with
large sky coverage, for which a 3D Spherical Fourier-Bessel (SFB) analysis in
spherical coordinates is natural. Wavelets are particularly well-suited to the
analysis and denoising of cosmological data, but a spherical 3D isotropic
wavelet transform does not currently exist to analyse spherical 3D data. The
aim of this paper is to present a new formalism for a spherical 3D isotropic
wavelet, i.e. one based on the SFB decomposition of a 3D field and accompany
the formalism with a public code to perform wavelet transforms. We describe a
new 3D isotropic spherical wavelet decomposition based on the undecimated
wavelet transform (UWT) described in Starck et al. 2006. We also present a new
fast Discrete Spherical Fourier-Bessel Transform (DSFBT) based on both a
discrete Bessel Transform and the HEALPIX angular pixelisation scheme. We test
the 3D wavelet transform and as a toy-application, apply a denoising algorithm
in wavelet space to the Virgo large box cosmological simulations and find we
can successfully remove noise without much loss to the large scale structure.
We have described a new spherical 3D isotropic wavelet transform, ideally
suited to analyse and denoise future 3D spherical cosmological surveys, which
uses a novel Discrete Spherical Fourier-Bessel Transform. We illustrate its
potential use for denoising using a toy model. All the algorithms presented in
this paper are available for download as a public code called MRS3D at
http://jstarck.free.fr/mrs3d.htmlComment: 9 pages + appendices. Public code can be downloaded at
http://jstarck.free.fr/mrs3d.html Corrected typos and updated references.
Accepted for publication in Astronomy and Astrophysic
The strong gravitational lens finding challenge
Large-scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images, and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects, reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100 000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. Having multi-band, ground based data is found to be better for this purpose than single-band space based data with lower noise and higher resolution, suggesting that multi-colour data is crucial. Multi-band space based data will be superior to ground based data. The most difficult challenge for a lens finder is differentiating between rare, irregular and ring-like face-on galaxies and true gravitational lenses. The degree to which the efficiency and biases of lens finders can be quantified largely depends on the realism of the simulated data on which the finders are trained
Comparative pharmacokinetics and bioavailability of albendazole sulfoxide in sheep and goats, and dose-dependent plasma disposition in goats
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