Unmasking the Masked Universe: the 2M++ catalogue through Bayesian eyes

TL;DR

This paper presents a comprehensive Bayesian analysis of the 2M++ galaxy survey, reconstructing large-scale structures and initial conditions to better understand the Universe's matter distribution.

Contribution

It introduces a two-step Bayesian framework for analyzing galaxy surveys, deriving biases, power spectra, and detailed 3D structures with self-consistent extrapolations.

Findings

Reconstructed the density field and initial conditions at z=1000.

Demonstrated the visibility of the Sloan Great Wall.

Detected and characterized the Local Void.

Abstract

This work describes a full Bayesian analysis of the Nearby Universe as traced by galaxies of the 2M++ survey. The analysis is run in two sequential steps. The first step self-consistently derives the luminosity dependent galaxy biases, the power-spectrum of matter fluctuations and matter density fields within a Gaussian statistic approximation. The second step makes a detailed analysis of the three dimensional Large Scale Structures, assuming a fixed bias model and a fixed cosmology. This second step allows for the reconstruction of both the final density field and the initial conditions at z=1000 assuming a fixed bias model. From these, we derive fields that self-consistently extrapolate the observed large scale structures. We give two examples of these extrapolation and their utility for the detection of structures: the visibility of the Sloan Great Wall, and the detection and characterization of the Local Void using DIVA, a Lagrangian based technique to classify structures.