Statistical Analysis of Galaxy Surveys-IV: An objective way to quantify the impact of superstructures on galaxy clustering statistics

TL;DR

This paper introduces a spatial Jackknife resampling method to accurately estimate errors in galaxy clustering measurements and assess the influence of large structures on galaxy surveys, enhancing the robustness of cosmological analyses.

Contribution

It presents a novel, objective Jackknife technique for error estimation and impact assessment of large structures in galaxy clustering data.

Findings

The method reliably estimates errors on two- and three-point correlation functions.

The frequency of outliers matches predictions from cosmological simulations.

Consistent clustering measurements are found between SDSS and 2dFGRS datasets.

Abstract

For galaxy clustering to provide robust constraints on cosmological parameters and galaxy formation models, it is essential to make reliable estimates of the errors on clustering measurements. We present a new technique, based on a spatial Jackknife (JK) resampling, which provides an objective way to estimate errors on clustering statistics. Our approach allows us to set the appropriate size for the Jackknife subsamples. The method also provides a means to assess the impact of individual regions on the measured clustering, and thereby to establish whether or not a given galaxy catalogue is dominated by one or several large structures, preventing it to be considered as a "fair sample". We apply this methodology to the two- and three-point correlation functions measured from a volume limited sample of M* galaxies drawn from data release seven of the Sloan Digital Sky Survey (SDSS). The frequency of jackknife subsample outliers in the data is shown to be consistent with that seen in large N-body simulations of clustering in the cosmological constant plus cold dark matter cosmology. We also present a comparison of the three-point correlation function in SDSS and 2dFGRS using this approach and find consistent measurements between the two samples.