Analysis of an iterative reconstruction method in comparison of the standard reconstruction method

TL;DR

This paper compares a new iterative density reconstruction algorithm with the standard method, showing comparable BAO parameter fitting and improved removal of redshift space distortions, promising for future large-scale surveys.

Contribution

The paper introduces and evaluates a new iterative reconstruction algorithm with a decreasing smoothing scale, extending it to include anisotropic smoothing and redshift space distortion correction.

Findings

Both algorithms produce consistent BAO parameter fits.

The iterative method better removes redshift space distortions.

Results are robust over various smoothing scales.

Abstract

We present a detailed analysis of a new, iterative density reconstruction algorithm. This algorithm uses a decreasing smoothing scale to better reconstruct the density field in Lagrangian space. We implement this algorithm to run on the Quijote simulations, and extend it to (a) include a smoothing kernel that smoothly goes from anisotropic to isotropic, and (b) a variant that does not correct for redshift space distortions. We compare the performance of this algorithm with the standard reconstruction method. Our examinations of the methods include cross-correlation of the reconstructed density field with the linear density field, reconstructed two-point functions, and BAO parameter fitting. We also examine the impact of various parameters, such as smoothing scale, anisotropic smoothing, tracer type/bias, and the inclusion of second order perturbation theory. We find that the two reconstruction algorithms are comparable in most of the areas we examine. In particular, both algorithms give consistent fittings of BAO parameters. The fits are robust over a range of smoothing scales. We find the iterative algorithm is significantly better at removing redshift space distortions. The new algorithm will be a promising method to be employed in the ongoing and future large-scale structure surveys.