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Leclercq, Florent

Publications and source records attributed to Leclercq, Florent.

The phase-space structure of nearby dark matter as constrained by the SDSS

Previous studies using numerical simulations have demonstrated that the shape of the cosmic web can be described by studying the Lagrangian displacement field. We extend these analyses, showing that it is now possible to perform a Lagrangian description of cosmic structure in the nearby Universe based on large-scale structure observations. Building upon recent Bayesian large-scale inference of initial conditions, we present a cosmographic analysis of the dark matter distribution and its evolution, referred to as the dark matter phase-space sheet, in the nearby universe as probed by the Sloan Digital Sky Survey main galaxy sample. We consider its stretchings and foldings using a tetrahedral tessellation of the Lagrangian lattice. The method provides extremely accurate estimates of nearby density and velocity fields, even in regions of low galaxy density. It also measures the number of matter streams, and the deformation and parity reversals of fluid elements, which were previously thought inaccessible using observations. We illustrate the approach by showing the phase-space structure of known objects of the nearby Universe such as the Sloan Great Wall, the Coma cluster and the Boötes void. We dissect cosmic structures into four distinct components (voids, sheets, filaments, and clusters), using the Lagrangian classifiers DIVA, ORIGAMI, and a new scheme which we introduce and call LICH. Because these classifiers use information other than the sheer local density, identified structures explicitly carry physical information about their formation history. Accessing the phase-space structure of dark matter in galaxy surveys opens the way for new confrontations of observational data and theoretical models. We have made our data products publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗

Comparing cosmic web classifiers using information theory

We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-WEB, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗