Paper

Iterative initial condition reconstruction

Schmittfull, Marcel, Baldauf, Tobias, Zaldarriaga, Matias

2020

Overview

Motivated by recent developments in perturbative calculations of the nonlinear evolution of large-scale structure, we present an iterative algorithm to reconstruct the initial conditions in a given volume starting from the dark matter distribution in real space. In our algorithm, objects are first moved back iteratively along estimated potential gradients, with a progressively reduced smoothing scale, until a nearly uniform catalog is obtained. The linear initial density is then estimated as the divergence of the cumulative displacement, with an optional second-order correction. This algorithm should undo nonlinear effects up to one-loop order, including the higher-order infrared resummation piece. We test the method using dark matter simulations in real space. At redshift \\(z=0\\), we find that after eight iterations the reconstructed density is more than \\(95\\%\\) correlated with the initial density at \\(k 0.35\\; hMpc^-1\\). The reconstruction also reduces the power in the difference between reconstructed and initial fields by more than 2 orders of magnitude at \\(k 0.2\\; hMpc^-1\\), and it extends the range of scales where the full broadband shape of the power spectrum matches linear theory by a factor of 2-3. As a specific application, we consider measurements of the baryonic acoustic oscillation (BAO) scale that can be improved by reducing the degradation effects of large-scale flows. In our idealized dark matter simulations, the method improves the BAO signal-to-noise ratio by a factor of 2.7 at \\(z=0\\) and by a factor of 2.5 at \\(z=0.6\\), improving standard BAO reconstruction by \\(70\\%\\) at \\(z=0\\) and \\(30\\%\\) at \\(z=0.6\\), and matching the optimal BAO signal and signal-to-noise ratio of the linear density in the same volume. For BAO, the iterative nature of the reconstruction is the most important aspect.

• Publisher: Cornell University Library, arXiv.org
• Subject: Acoustic noise / Algorithms / Broadband / Computer simulation / Dark matter / Density / Divergence / Initial conditions / Iterative algorithms / Large scale structure of the universe / Object motion / Potential gradient / Reconstruction / Red shift / Signal to noise ratio / Test procedures