Modeling the reconstructed BAO in Fourier space

TL;DR

This paper evaluates the effectiveness of density field reconstruction techniques in enhancing BAO signals in galaxy surveys, optimizing smoothing parameters, and improving BAO position accuracy using mock data and theoretical models.

Contribution

It introduces a detailed analysis of BAO reconstruction efficiency, compares different reconstruction conventions, and proposes improved Fourier space BAO fitting models based on perturbation theory.

Findings

Optimal Gaussian smoothing scale identified for BOSS CMASS.

Reconstructed BAO signals show reduced damping and improved position accuracy.

Modified BAO fitting models enhance fit quality and parameter estimation.

Abstract

The density field reconstruction technique, which was developed to partially reverse the nonlinear degradation of the Baryon Acoustic Oscillation (BAO) feature in the galaxy redshift surveys, has been successful in substantially improving the cosmology constraints from recent galaxy surveys such as Baryon Oscillation Spectroscopic Survey (BOSS). We estimate the efficiency of the reconstruction method as a function of various reconstruction details. To directly quantify the BAO information in nonlinear density fields before and after reconstruction, we calculate the cross-correlations (i.e., propagators) of the pre(post)-reconstructed density field with the initial linear field using a mock galaxy sample that is designed to mimic the clustering of the BOSS CMASS galaxies. The results directly provide the BAO damping as a function of wavenumber that can be implemented into the Fisher matrix analysis. We focus on investigating the dependence of the propagator on a choice of smoothing filters and on two major different conventions of the redshift-space density field reconstruction that have been used in literature. By estimating the BAO signal-to-noise for each case, we predict constraints on the angular diameter distance and Hubble parameter using the Fisher matrix analysis. We thus determine an optimal Gaussian smoothing filter scale for the signal-to-noise level of the BOSS CMASS. We also present appropriate BAO fitting models for different reconstruction methods based on the first and second order Lagrangian perturbation theory in Fourier space. Using the mock data, we show that the modified BAO fitting model can substantially improve the accuracy of the BAO position in the best fits as well as the goodness of the fits.