Cosmology with weak-lensing peak counts

Abstract

English versionWeak gravitational lensing (WL) causes distortions of galaxy images and probes massive structures on large scales, allowing us to understand the late-time evolution of the Universe. One way to extract the cosmological information from WL is to use peak statistics. Peaks are tracers of massive halos and therefore probe the mass function. They retain non-Gaussian information and have already been shown as a promising tool to constrain cosmology. In this work, we develop a new model to predict WL peak counts. The model generates fast simulations based on halo sampling and selects peaks from the derived lensing maps. This approach has three main advantages. First, the model is very fast: only several seconds are required to perform a realization. Second, including realistic conditions is straightforward. Third, the model provides the full distribution information because of its stochasticity. We show that our model agrees well with N-body simulations. Then, we study the impacts of the cosmology-dependent covariance on constraints and explore different parameter inference methods. A special focus is put on approximate Bayesian computation (ABC), an acceptreject sampler without the need to estimate the likelihood. We show that ABC is able to yield robust constraints with much reduced time costs. Several filtering techniques are studied to improve the extraction of multiscale information. Finally, the new model is applied to the CFHTLenS, KiDS DR1/2, and DES SV data sets. Our preliminary results agree with the Planck constraints assuming the Lambda-CDM model. Overall, the thesis forges an innovative tool for future WL surveys. French versionL'effet de lentille gravitionnelle faible (WL) déforme les images des galaxies observées. Il porte des informations sur les grandes structures et nous apprend l'évolution de l'Univers. Une façon d'extraire des informations de WL est d'utiliser les statistiques d'ordres supérieurs à deux, en particulier le comptage de pics. Les pics indiquent la présence des halos et mesurent la fonction de masse. Ils sont considérés comme un bon outil pour contraindre la cosmologie. Dans cette thèse, on développe un nouveau modèle de prédiction sur le nombre des pics. Celui-ci génère des simulations des halos et sélectionne les pics à partir de la carte de WL résultante. Cette approche jouit de trois avantages. D'abord, le modèle est rapide: une réalisation s'obtient en quelques secondes. Ensuite, il est aisé d'y inclure les effets observationnels. Enfin, on a accès à la distribution complète des observables grâce à son caractère stochastique. On montre que notre modèle est en accord avec les simulations à N-corps. Puis, on examine des méthodes de contraintes variées. Un accent est mis sur la computation bayesienne approximative (ABC) qui est un processus d'acceptation/rejet ne nécessitant pas d'évaluer la fonction de vraisemblance. On montre que ABC fournit des contraintes cohérentes en un temps plus faible que la méthode classique. Des méthodes de filtrage, pour améliorer l'extractio...

Sparse Bayesian mass mapping with uncertainties: peak statistics and feature locations

“…For a more mathematical background of the field, with emphasis on statistical methods see Grimm & Yoo (2018); Munshi et al (2008); Heavens (2009). For a background of specifically the peak statistics see Lin (2016).…”

“…In this article we choose to define a peak in κ map by a pixel κ map (x) which is larger than the 8 pixels which surround it (Lin 2016). A point of the peak statistic is computed as follows: A threshold K is taken on κ map , and the peak count (number of peaks which have intensity larger than K) is taken on the sub-set of pixels larger than the threshold.…”

Quantifying systematics from the shear inversion on weak-lensing peak counts

“…To cite a few, potential sources of systematics are shape measurement errors (Cardone et al 2014), photometric redshift uncertainty (Cunha et al 2014;Bonnett et al 2016;Choi et al 2016;Gruen & Brimioulle 2017), intrinsic alignment of galaxies (Chisari et al 2014;Codis et al 2015;Schaefer & Merkel 2015;Schrabback et al 2015;Krause et al 2016), baryon physics (Mohammed et al 2014;Harnois-Déraps et al 2015), and instrumental responses (Gurvich & Mandelbaum 2016;Okura et al 2016;Kannawadi et al 2016;Plazas et al 2016). Concerning WL peak counts, studies of systematics have been done by Yang et al (2013) and Osato et al (2015) for baryon physics, Liu, X. et al (2014) for masking, and Liu, J. et al (2014) for magnification bias (see also Lin 2016 for a review of WL-peak-related studies). These existing studies are not sufficient for modeling the peak statistics at high precision.…”

“…Two algorithms, the Kaiser-Squire and Seitz-Schneider inversions, are considered in this study. We use our stochastic model developed in Paper I, Lin &Kilbinger (2015b, Paper II), andLin et al (2016, Paper III) to examine this effect. The stochasticity of the model makes including different inversions and different mapmaking methods straightforward.…”