Reconstructing the Initial Density Field of the Local Universe: Method and Test with Mock Catalogs
Huiyuan Wang, H. J. Mo, Xiaohu Yang, Frank C. van den Bosch

TL;DR
This paper introduces a Hamiltonian Markov Chain Monte Carlo method to reconstruct the initial density field of the local universe from current galaxy data, accurately recovering phases and amplitudes in mock catalogs.
Contribution
The paper presents a novel Bayesian reconstruction method that effectively recovers initial density fields from present-day galaxy distributions using mock data.
Findings
Method accurately recovers initial density amplitudes and phases.
Reconstructed density fields match original simulations within specified density ranges.
Fourier phases of reconstructed fields are tightly correlated with original simulations.
Abstract
Our research objective in this paper is to reconstruct an initial linear density field, which follows the multivariate Gaussian distribution with variances given by the linear power spectrum of the current CDM model and evolves through gravitational instability to the present-day density field in the local Universe. For this purpose, we develop a Hamiltonian Markov Chain Monte Carlo method to obtain the linear density field from a posterior probability function that consists of two components: a prior of a Gaussian density field with a given linear spectrum, and a likelihood term that is given by the current density field. The present-day density field can be reconstructed from galaxy groups using the method developed in Wang et al. (2009a). Using a realistic mock SDSS DR7, obtained by populating dark matter haloes in the Millennium simulation with galaxies, we show that our method can…
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