Dark Energy Survey Year 3 Results: Measurement of the Baryon Acoustic Oscillations with Three-dimensional Clustering

TL;DR

This paper measures the BAO scale using three-dimensional clustering in DES Year 3 data, employing a novel Gaussian stacking window to improve signal robustness and compare with angular correlation results.

Contribution

It introduces a method using the projected three-dimensional correlation function with a Gaussian window for BAO measurement in galaxy surveys, accounting for realistic redshift distributions.

Findings

BAO scale constrained to 19.15 ± 0.58 at z=0.835

Gaussian window yields more robust BAO results than top-hat

Three-dimensional clustering results are more sensitive to photo-z errors

Abstract

The three-dimensional correlation function offers an effective way to summarize the correlation of the large-scale structure even for imaging galaxy surveys. We have applied the projected three-dimensional correlation function, $\xi_{\rm p}$ to measure the Baryonic Acoustic Oscillations (BAO) scale on the first-three years Dark Energy Survey data. The sample consists of about 7 million galaxies in the redshift range $ 0.6 < z_{\rm p } < 1.1 $ over a footprint of $4108 \, \mathrm{deg}^2 $. Our theory modeling includes the impact of realistic true redshift distributions beyond Gaussian photo-$z$ approximation. To increase the signal-to-noise of the measurements, a Gaussian stacking window function is adopted in place of the commonly used top-hat. Using the full sample, $ D_{\rm M}(z_{\rm eff} ) / r_{\rm s} $, the ratio between the comoving angular diameter distance and the sound horizon, is constrained to be $ 19.00 \pm 0.67 $ (top-hat) and $ 19.15 \pm 0.58 $ (Gaussian) at $z_{\rm eff} = 0.835$. The constraint is weaker than the angular correlation $w$ constraint ($18.84 \pm 0.50$) because the BAO signals are heterogeneous across redshift. When a homogeneous BAO-signal sub-sample in the range $ 0.7 < z_{\rm p } < 1.0 $ ($z_{\rm eff} = 0.845$) is considered, $\xi_{\rm p} $ yields $ 19.80 \pm 0.67 $ (top-hat) and $ 19.84 \pm 0.53 $ (Gaussian). The latter is mildly stronger than the $w$ constraint ($19.86 \pm 0.55 $). We find that the $\xi_{\rm p} $ results are more sensitive to photo-$z$ errors than $w$ because $\xi_{\rm p}$ keeps the three-dimensional clustering information causing it to be more prone to photo-$z$ noise. The Gaussian window gives more robust results than the top-hat as the former is designed to suppress the low signal modes. $\xi_{\rm p}$ and the angular statistics such as $w$ have their own pros and cons, and they serve an important crosscheck with each other.