Cosmic Structure and Dynamics of the Local Universe

TL;DR

This paper uses a Bayesian machine learning approach with 2LPT to reconstruct the local universe's structure and dynamics from galaxy survey data, achieving high accuracy in identifying nonlinear features like filaments and voids.

Contribution

It introduces a novel Bayesian network method combining 2LPT for detailed, self-consistent reconstruction of local cosmic structures and velocities from galaxy surveys.

Findings

High accuracy in density and velocity field reconstruction up to k ~ 1 h Mpc^-1

Reliable results at scales of 3-4 h^-1 Mpc

Local group motion consistent with LambdaCDM predictions

Abstract

We present a cosmography analysis of the Local Universe based on the recently released Two-Micron All-Sky Redshift Survey (2MRS). Our method is based on a Bayesian Networks Machine Learning algorithm (the Kigen-code) which self-consistently samples the initial density fluctuations compatible with the observed galaxy distribution and a structure formation model given by second order Lagrangian perturbation theory (2LPT). From the initial conditions we obtain an ensemble of reconstructed density and peculiar velocity fields which characterize the local cosmic structure with high accuracy unveiling nonlinear structures like filaments and voids in detail. Coherent redshift space distortions are consistently corrected within 2LPT. From the ensemble of cross-correlations between the reconstructions and the galaxy field and the variance of the recovered density fields we find that our method is extremely accurate up to k ~ 1 h Mpc^-1 and still yields reliable results down to scales of about 3-4 h^-1 Mpc. The motion of the local group we obtain within ~ 80 h^-1 Mpc (v_LG=522+-86 km s^-1, l_LG=291^o +- 16^o, b_LG=34^o+-8^o) is in good agreement with measurements derived from the CMB and from direct observations of peculiar motions and is consistent with the predictions of LambdaCDM.