Jean-François Cardoso

Pierre and Marie Curie University - Paris 6, Lutetia Parisorum, Île-de-France, France

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Publications (9)10.61 Total impact

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    ABSTRACT: 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.
    09/2014;
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    ABSTRACT: 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 $\Omega_ch^2$ better than TT by 15%, while determining $\Omega_bh^2$, $n_s$ and $\theta$ 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.
    03/2014;
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    ABSTRACT: A large-scale hydrodynamical cosmological simulation, Horizon-AGN, is used to investigate the alignment between the spin of galaxies and the large-scale cosmic filaments above redshift one. The analysis of more than 150 000 galaxies with morphological diversity in a 100 Mpc/h comoving box size shows that the spin of low-mass, rotation-dominated, blue, star-forming galaxies is preferentially aligned with their neighbouring filaments. High-mass, dispersion-dominated, red, quiescent galaxies tend to have a spin perpendicular to nearby filaments. The reorientation of the spin of massive galaxies is provided by galaxy mergers which are significant in the mass build up of high-mass galaxies. We find that the stellar mass transition from alignment to misalignment happens around 3.10^10 M_sun. This is consistent with earlier findings of a dark matter mass transition for the orientation of the spin of halos (5.10^11 M_sun at the same redshift from Codis et al., 2012). With these numerical evidence, we advocate a scenario in which galaxies form in the vorticity-rich neighbourhood of filaments, and migrate towards the nodes of the cosmic web as they convert their orbital angular momentum into spin. The signature of this process can be traced to the physical and morphological properties of galaxies, as measured relative to the cosmic web. We argue that a strong source of feedback such as Active Galactic Nuclei is mandatory to quench in situ star formation in massive galaxies. It allows mergers to play their key role by reducing post-merger gas inflows and, therefore, keeping galaxy spins misaligned with cosmic filaments. It also promotes diversity amongst galaxy properties.
    02/2014;
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    ABSTRACT: 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.
    Astronomy and Astrophysics 01/2013; · 5.08 Impact Factor
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    ABSTRACT: CosmoPMC is a Monte-Carlo sampling method to explore the likelihood of various cosmological probes. The sampling engine is implemented with the package pmclib. It is called Population MonteCarlo (PMC), which is a novel technique to sample from the posterior. PMC is an adaptive importance sampling method which iteratively improves the proposal to approximate the posterior. This code has been introduced, tested and applied to various cosmology data sets.
    Astrophysics Source Code Library. 12/2012;
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    ABSTRACT: We present the public release of the Bayesian sampling algorithm for cosmology, CosmoPMC (Cosmology Population Monte Carlo). CosmoPMC explores the parameter space of various cosmological probes, and also provides a robust estimate of the Bayesian evidence. CosmoPMC is based on an adaptive importance sampling method called Population Monte Carlo (PMC). Various cosmology likelihood modules are implemented, and new modules can be added easily. The importance-sampling algorithm is written in C, and fully parallelised using the Message Passing Interface (MPI). Due to very little overhead, the wall-clock time required for sampling scales approximately with the number of CPUs. The CosmoPMC package contains post-processing and plotting programs, and in addition a Monte-Carlo Markov chain (MCMC) algorithm. The sampling engine is implemented in the library pmclib, and can be used independently. The software is available for download at http://www.cosmopmc.info.
    01/2011;
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    ABSTRACT: 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 MNRAS
    Monthly Notices of the Royal Astronomical Society 12/2009; · 5.52 Impact Factor
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    ABSTRACT: Article 023507 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. oui
    Physical review D: Particles and fields 07/2009;
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    ABSTRACT: 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 figures
    Physical review D: Particles and fields 03/2009;