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Alexandre Refregier

Publications and source records attributed to Alexandre Refregier.

At least 109 records · Page 6Linked to original sources

Information Gains from Cosmic Microwave Background Experiments

To shed light on the fundamental problems posed by Dark Energy and Dark Matter, a large number of experiments have been performed and combined to constrain cosmological models. We propose a novel way of quantifying the information gained by updates on the parameter constraints from a series of experiments which can either complement earlier measurements or replace them. For this purpose, we use the Kullback-Leibler divergence or relative entropy from information theory to measure differences in the posterior distributions in model parameter space from a pair of experiments. We apply this formalism to a historical series of Cosmic Microwave Background experiments ranging from Boomerang to WMAP, SPT, and Planck. Considering different combinations of these experiments, we thus estimate the information gain in units of bits and distinguish contributions from the reduction of statistical errors and the `surprise' corresponding to a significant shift of the parameters' central values. For this experiment series, we find individual relative entropy gains ranging from about 1 to 30 bits. In some cases, e.g. when comparing WMAP and Planck results, we find that the gains are dominated by the surprise rather than by improvements in statistical precision. We discuss how this technique provides a useful tool for both quantifying the constraining power of data from cosmological probes and detecting the tensions between experiments.

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Gravitational lens modeling with basis sets

We present a strong lensing modeling technique based on versatile basis sets for the lens and source planes. Our method uses high performance Monte Carlo algorithms, allows for an adaptive build up of complexity and bridges the gap between parametric and pixel based reconstruction methods. We apply our method to a HST image of the strong lens system RXJ1131-1231 and show that our method finds a reliable solution and is able to detect substructure in the lens and source planes simultaneously. Using mock data we show that our method is sensitive to sub-clumps with masses four orders of magnitude smaller than the main lens, which corresponds to about $10^8 M_{\odot}$, without prior knowledge on the position and mass of the sub-clump. The modelling approach is flexible and maximises automation to facilitate the analysis of the large number of strong lensing systems expected in upcoming wide field surveys. The resulting search for dark sub-clumps in these systems, without mass-to-light priors, offers promise for probing physics beyond the standard model in the dark matter sector.

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Approximate Bayesian Computation for Forward Modeling in Cosmology

Bayesian inference is often used in cosmology and astrophysics to derive constraints on model parameters from observations. This approach relies on the ability to compute the likelihood of the data given a choice of model parameters. In many practical situations, the likelihood function may however be unavailable or intractable due to non-gaussian errors, non-linear measurements processes, or complex data formats such as catalogs and maps. In these cases, the simulation of mock data sets can often be made through forward modeling. We discuss how Approximate Bayesian Computation (ABC) can be used in these cases to derive an approximation to the posterior constraints using simulated data sets. This technique relies on the sampling of the parameter set, a distance metric to quantify the difference between the observation and the simulations and summary statistics to compress the information in the data. We first review the principles of ABC and discuss its implementation using a Population Monte-Carlo (PMC) algorithm and the Mahalanobis distance metric. We test the performance of the implementation using a Gaussian toy model. We then apply the ABC technique to the practical case of the calibration of image simulations for wide field cosmological surveys. We find that the ABC analysis is able to provide reliable parameter constraints for this problem and is therefore a promising technique for other applications in cosmology and astrophysics. Our implementation of the ABC PMC method is made available via a public code release.

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Calibrated Ultra Fast Image Simulations for the Dark Energy Survey

Weak lensing by large-scale structure is a powerful technique to probe the dark components of the universe. To understand the measurement process of weak lensing and the associated systematic effects, image simulations are becoming increasingly important. For this purpose we present a first implementation of the $\textit{Monte Carlo Control Loops}$ ($\textit{MCCL}$; Refregier & Amara 2014), a coherent framework for studying systematic effects in weak lensing. It allows us to model and calibrate the shear measurement process using image simulations from the Ultra Fast Image Generator (UFig; Berge et al. 2013). We apply this framework to a subset of the data taken during the Science Verification period (SV) of the Dark Energy Survey (DES). We calibrate the UFig simulations to be statistically consistent with DES images. We then perform tolerance analyses by perturbing the simulation parameters and study their impact on the shear measurement at the one-point level. This allows us to determine the relative importance of different input parameters to the simulations. For spatially constant systematic errors and six simulation parameters, the calibration of the simulation reaches the weak lensing precision needed for the DES SV survey area. Furthermore, we find a sensitivity of the shear measurement to the intrinsic ellipticity distribution, and an interplay between the magnitude-size and the pixel value diagnostics in constraining the noise model. This work is the first application of the $\textit{MCCL}$ framework to data and shows how it can be used to methodically study the impact of systematics on the cosmic shear measurement.

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Theoretical and observational constraints on the HI intensity power spectrum

Mapping of the neutral hydrogen (HI) 21-cm intensity fluctuations across redshifts promises a novel and powerful probe of cosmology. The neutral hydrogen gas mass density, $Ω_{\rm HI}$ and bias parameter, $b_{\rm HI}$ are key astrophysical inputs to the HI intensity fluctuation power spectrum. We compile the latest theoretical and observational constraints on $Ω_{\rm HI}$ and $b_{\rm HI}$ at various redshifts in the post-reionization universe. Constraints are incorporated from galaxy surveys, HI intensity mapping experiments, damped Lyman-$α$ system observations, theoretical prescriptions for assigning HI to dark matter halos, and the results of numerical simulations. Using a minimum variance interpolation scheme, we obtain the predicted uncertainties on the HI intensity fluctuation power spectrum across redshifts 0-3.5 for three different confidence scenarios. We provide a convenient tabular form for the interpolated values of $Ω_{\rm HI}$, $b_{\rm HI}$ and the HI power spectrum amplitude and their uncertainties. We discuss the consequences for the measurement of the power spectrum by current and future intensity mapping experiments.

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HOPE: A Python Just-In-Time compiler for astrophysical computations

The Python programming language is becoming increasingly popular for scientific applications due to its simplicity, versatility, and the broad range of its libraries. A drawback of this dynamic language, however, is its low runtime performance which limits its applicability for large simulations and for the analysis of large data sets, as is common in astrophysics and cosmology. While various frameworks have been developed to address this limitation, most focus on covering the complete language set, and either force the user to alter the code or are not able to reach the full speed of an optimised native compiled language. In order to combine the ease of Python and the speed of C++, we developed HOPE, a specialised Python just-in-time (JIT) compiler designed for numerical astrophysical applications. HOPE focuses on a subset of the language and is able to translate Python code into C++ while performing numerical optimisation on mathematical expressions at runtime. To enable the JIT compilation, the user only needs to add a decorator to the function definition. We assess the performance of HOPE by performing a series of benchmarks and compare its execution speed with that of plain Python, C++ and the other existing frameworks. We find that HOPE improves the performance compared to plain Python by a factor of 2 to 120, achieves speeds comparable to that of C++, and often exceeds the speed of the existing solutions. We discuss the differences between HOPE and the other frameworks, as well as future extensions of its capabilities. The fully documented HOPE package is available at http://hope.phys.ethz.ch and is published under the GPLv3 license on PyPI and GitHub.

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3-dimensional spherical analyses of cosmological spectroscopic surveys

Spectroscopic redshift surveys offer great prospects for constraining the dark sector in cosmology. Future surveys will however be both deep and wide and will thus require an analysis in 3-dimensional spherical geometry. We review and compare several methods which have been proposed in the literature for this purpose, focusing in particular on implementations of the spherical harmonic tomography (SHT) power spectrum $C^{i j}_{l}$ and the spherical Fourier Bessel (SFB) power spectrum $C_{l} (k, k')$. Using a Fisher analysis, we compare the forecasted constraints on cosmological parameters using these statistics. These constraints typically rely on approximations such as the Limber approximation and make specific choices in the numerical implementation of each statistic. Using a series of toy models, we explore the applicability of these approximations and study the sensitivity of the SHT and SFB statistics to the details of their implementation. In particular, we show that overlapping redshift bins may improve cosmological constraints using the SHT statistic when the number of bins is small, and that the SFB constraints are quite robust to changes in the assumed distance-redshift relation. We also find that the SHT can be tailored to be more sensitive to modes at redshifts close to the survey boundary, while the SFB appears better suited to capture information beyond the smooth shape of the power spectrum. In this context, we discuss the pros and cons of the different techniques and their impact on the design and analysis of future wide field spectroscopic surveys.

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A simple model linking galaxy and dark matter evolution

We construct a simple phenomenological model for the evolving galaxy population by incorporating pre-defined baryonic prescriptions into a dark matter hierarchical merger tree. Specifically the model is based on the simple gas-regulator model introduced by Lilly et al. 2013 coupled with the empirical quenching rules of Peng et al. 2010/12. The simplest model already does quite well in reproducing, without re-adjusting the input parameters, many observables including the Main Sequence sSFR-mass relation, the faint end slope of the galaxy mass function and the shape of the star-forming and passive mass functions. Compared with observations and/or the recent phenomenological model of Behroozi et al. 2013 based on epoch-dependent abundance-matching, our model also qualitatively reproduces the evolution of the Main Sequence sSFR(z) and SFRD(z) star formation rate density relations, the $M_s - M_h$ stellar-to-halo mass relation and also the $SFR - M_h$ relation. Quantitatively the evolution of sSFR(z) and SFRD(z) is not steep enough, the $M_s - M_h$ relation is not quite peaked enough and, surprisingly, the ratio of quenched to star-forming galaxies around M* is not quite high enough. We show that these deficiencies can simultaneously be solved by ad hoc allowing galaxies to re-ingest some of the gas previously expelled in winds, provided that this is done in a mass-dependent and epoch-dependent way. These allow the model galaxies to reduce an inherent tendency to saturate their star-formation efficiency. This emphasizes how efficient galaxies around M* are in converting baryons into stars and highlights the fact that quenching occurs just at the point when galaxies are rapidly approaching the maximum possible efficiency of converting baryons into stars.

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A Way Forward for Cosmic Shear: Monte-Carlo Control Loops

Weak lensing by large scale structure or 'cosmic shear' is a potentially powerful cosmological probe to shed new light on Dark Matter, Dark Energy and Modified Gravity. It is based on the weak distortions induced by large-scale structures on the observed shapes of distant galaxies through gravitational lensing. While the potentials of this purely gravitational effect are great, results from this technique have been hampered because the measurement of this weak effect is difficult and limited by systematics effects. In particular, a demanding step is the measurement of the weak lensing shear from wide field CCD images of galaxies. We describe the origin of the problem and propose a way forward for cosmic shear. Our proposed approach is based on Monte-Carlo Control Loops and draws upon methods widely used in particle physics and engineering. We describe the control loop scheme and show how it provides a calibration method based on fast image simulations tuned to reproduce the statistical properties of a specific cosmic shear data set. Through a series of iterative loops and diagnostic tests, the Monte Carlo image simulations are made robust to perturbations on modeling input parameters and thus to systematic effects. We discuss how this approach can make the problem tractable and unleash to full potential of cosmic shear for cosmology.

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Model Breaking Measure for Cosmological Surveys

Recent observations have led to the establishment of the concordance LCDM model for cosmology. A number of experiments are being planned to shed light on dark energy, dark matter, inflation and gravity, which are the key components of the model. To optimize and compare the reach of these surveys, several figures of merit have been proposed. They are based on either the forecasted precision on the LCDM model and its expansion, or on the expected ability to distinguish two models. We propose here another figure of merit that quantifies the capacity of future surveys to rule out the LCDM model. It is based on a measure of the difference in volume of observable space that the future surveys will constrain with and without imposing the model. This model breaking figure of merit is easy to compute and can lead to different survey optimizations than other metrics. We illustrate its impact using a simple combination of supernovae and BAO mock observations and compare the respective merit of these probes to challenge LCDM. We discuss how this approach would impact the design of future cosmological experiments.

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CosmoHammer: Cosmological parameter estimation with the MCMC Hammer

We study the benefits and limits of parallelised Markov chain Monte Carlo (MCMC) sampling in cosmology. MCMC methods are widely used for the estimation of cosmological parameters from a given set of observations and are typically based on the Metropolis-Hastings algorithm. Some of the required calculations can however be computationally intensive, meaning that a single long chain can take several hours or days to calculate. In practice, this can be limiting, since the MCMC process needs to be performed many times to test the impact of possible systematics and to understand the robustness of the measurements being made. To achieve greater speed through parallelisation, MCMC algorithms need to have short auto-correlation times and minimal overheads caused by tuning and burn-in. The resulting scalability is hence influenced by two factors, the MCMC overheads and the parallelisation costs. In order to efficiently distribute the MCMC sampling over thousands of cores on modern cloud computing infrastructure, we developed a Python framework called CosmoHammer which embeds emcee, an implementation by Foreman-Mackey et al. (2012) of the affine invariant ensemble sampler by Goodman and Weare (2010). We test the performance of CosmoHammer for cosmological parameter estimation from cosmic microwave background data. While Metropolis-Hastings is dominated by overheads, CosmoHammer is able to accelerate the sampling process from a wall time of 30 hours on a dual core notebook to 16 minutes by scaling out to 2048 cores. Such short wall times for complex data sets opens possibilities for extensive model testing and control of systematics.

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Space-quality data from balloon-borne telescopes: the High Altitude Lensing Observatory (HALO)

We present a method for attaining sub-arcsecond pointing stability during sub- orbital balloon flights, as designed for in the High Altitude Lensing Observatory (HALO) concept. The pointing method presented here has the potential to perform near-space quality optical astronomical imaging at 1-2% of the cost of space-based missions. We also discuss an architecture that can achieve sufficient thermomechanical stability to match the pointing stability. This concept is motivated by advances in the development and testing of Ultra Long Duration Balloon (ULDB) flights which promise to allow observation campaigns lasting more than three months. The design incorporates a multi-stage pointing architecture comprising: a gondola coarse azimuth control system, a multi-axis nested gimbal frame structure with arcsecond stability, a telescope de-rotator to eliminate field rotation, and a fine guidance stage consisting of both a telescope mounted angular rate sensor and guide CCDs in the focal plane to drive a fast-steering mirror. We discuss the results of pointing tests together with a preliminary thermo-mechanical analysis required for sub-arcsecond pointing at high altitude. Possible future applications in the areas of wide-field surveys and exoplanet searches are also discussed.

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Noise bias in weak lensing shape measurements

Weak lensing experiments are a powerful probe of cosmology through their measurement of the mass distribution of the universe. A challenge for this technique is to control systematic errors that occur when measuring the shapes of distant galaxies. In this paper we investigate noise bias, a systematic error that arises from second order noise terms in the shape measurement process. We first derive analytical expressions for the bias of general Maximum Likelihood Estimators (MLEs) in the presence of additive noise. We then find analytical expressions for a simplified toy model in which galaxies are modeled and fitted with a Gaussian with its size as a single free parameter. Even for this very simple case we find a significant effect. We also extend our analysis to a more realistic 6-parameter elliptical Gaussian model. We find that the noise bias is generically of the order of the inverse-squared signal-to-noise ratio (SNR) of the galaxies and is thus of the order of a percent for galaxies of SNR of 10, i.e. comparable to the weak lensing shear signal. This is nearly two orders of magnitude greater than the systematics requirements for future all-sky weak lensing surveys. We discuss possible ways to circumvent this effect, including a calibration method using simulations discussed in an associated paper.

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Measurement and Calibration of Noise Bias in Weak Lensing Galaxy Shape Estimation

Weak gravitational lensing has the potential to constrain cosmological parameters to high precision. However, as shown by the Shear TEsting Programmes (STEP) and GRavitational lEnsing Accuracy Testing (GREAT) Challenges, measuring galaxy shears is a nontrivial task: various methods introduce different systematic biases which have to be accounted for. We investigate how pixel noise on the image affects the bias on shear estimates from a Maximum-Likelihood forward model-fitting approach using a sum of co-elliptical Sérsic profiles, in complement to the theoretical approach of an an associated paper. We evaluate the bias using a simple but realistic galaxy model and find that the effects of noise alone can cause biases of order 1-10% on measured shears, which is significant for current and future lensing surveys. We evaluate a simulation-based calibration method to create a bias model as a function of galaxy properties and observing conditions. This model is then used to correct the simulated measurements. We demonstrate that this method can effectively reduce noise bias so that shear measurement reaches the level of accuracy required for estimating cosmic shear in upcoming lensing surveys.

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iCosmo: an Interactive Cosmology Package

Aims: The interactive software package iCosmo, designed to perform cosmological calculations is described. Methods: iCosmo is a software package to perform interactive cosmological calculations for the low redshift universe. Computing distance measures, the matter power spectrum, and the growth factor is supported for any values of the cosmological parameters. It also computes derived observed quantities for several cosmological probes such as cosmic shear, baryon acoustic oscillations and type Ia supernovae. The associated errors for these observables can be derived for customised surveys, or for pre-set values corresponding to current or planned instruments. The code also allows for the calculation of cosmological forecasts with Fisher matrices which can be manipulated to combine different surveys and cosmological probes. The code is written in the IDL language and thus benefits from the convenient interactive features and scientific library available in this language. iCosmo can also be used as an engine to perform cosmological calculations in batch mode, and forms a convenient adaptive platform for the development of further cosmological modules. With its extensive documentation, it may also serve as a useful resource for teaching and for newcomers in the field of cosmology. Results: The iCosmo package is described with various examples and command sequences. The code is freely available with documentation at http://www.icosmo.org, along with an interactive web interface and is part of the Initiative for Cosmology, a common archive for cosmological resources.

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Optimal capture of non-Gaussianity in weak lensing surveys: power spectrum, bispectrum and halo counts

We compare the efficiency of weak lensing-selected galaxy clusters counts and of the weak lensing bispectrum at capturing non-Gaussian features in the dark matter distribution. We use the halo model to compute the weak lensing power spectrum, the bispectrum and the expected number of detected clusters, and derive constraints on cosmological parameters for a large, low systematic weak lensing survey, by focusing on the $Ω_m$-$σ_8$ plane and on the dark energy equation of state. We separate the power spectrum into the resolved and the unresolved parts of the data, the resolved part being defined as detected clusters, and the unresolved part as the rest of the field. We consider four kinds of clusters counts, taking into account different amount of information : signal-to-noise ratio peak counts; counts as a function of clusters' mass; counts as a function of clusters' redshift; and counts as a function of clusters' mass and redshift. We show that when combined with the power spectrum, those four kinds of counts provide similar constraints, thus allowing one to perform the most direct counts, signal-to-noise peaks counts, and get percent level constraints on cosmological parameters. We show that the weak lensing bispectrum gives constraints comparable to those given by the power spectrum and captures non-Gaussian features as well as clusters counts, its combination with the power spectrum giving errors on cosmological parameters that are similar to, if not marginally smaller than, those obtained when combining the power spectrum with cluster counts. We finally note that in order to reach its potential, the weak lensing bispectrum must be computed using all triangle configurations, as equilateral triangles alone do not provide useful information.

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Weak lensing forecasts for dark energy, neutrinos and initial conditions

Weak gravitational lensing provides a sensitive probe of cosmology by measuring the mass distribution and the geometry of the low redshift universe. We show how an all-sky weak lensing tomographic survey can jointly constrain different sets of cosmological parameters describing dark energy, massive neutrinos (hot dark matter), and the primordial power spectrum. In order to put all sectors on an equal footing, we introduce a new parameter $β$, the second order running spectral index. Using the Fisher matrix formalism with and without CMB priors, we examine how the constraints vary as the parameter set is enlarged. We find that weak lensing with CMB priors provides robust constraints on dark energy parameters and can simultaneously provide strong constraints on all three sectors. We find that the dark energy sector is largely insensitive to the inclusion of the other cosmological sectors. Implications for the planning of future surveys are discussed.

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Results of the GREAT08 Challenge: An image analysis competition for cosmological lensing

We present the results of the GREAT08 Challenge, a blind analysis challenge to infer weak gravitational lensing shear distortions from images. The primary goal was to stimulate new ideas by presenting the problem to researchers outside the shear measurement community. Six GREAT08 Team methods were presented at the launch of the Challenge and five additional groups submitted results during the 6 month competition. Participants analyzed 30 million simulated galaxies with a range in signal to noise ratio, point-spread function ellipticity, galaxy size, and galaxy type. The large quantity of simulations allowed shear measurement methods to be assessed at a level of accuracy suitable for currently planned future cosmic shear observations for the first time. Different methods perform well in different parts of simulation parameter space and come close to the target level of accuracy in several of these. A number of fresh ideas have emerged as a result of the Challenge including a re-examination of the process of combining information from different galaxies, which reduces the dependence on realistic galaxy modelling. The image simulations will become increasingly sophisticated in future GREAT challenges, meanwhile the GREAT08 simulations remain as a benchmark for additional developments in shear measurement algorithms.

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