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

TL;DR

This paper introduces a novel Lagrangian-based method to analyze the phase-space structure of dark matter in the nearby universe using SDSS data, revealing detailed cosmic web features and object classifications.

Contribution

It extends simulation-based analyses to observational data, providing a new approach to map dark matter phase-space structure with high accuracy and physical insight.

Findings

Accurate density and velocity field estimates in low-density regions.

Identification of matter streams, deformation, and parity reversals from observations.

Dissection of cosmic structures into voids, sheets, filaments, and clusters using new classifiers.

Abstract

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 Bo\"otes 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.