摘要: We present a smoothed density-corrected $V_{\rm max}$ technique for building
a random catalog for property-dependent galaxy clustering estimation. This
approach is essentially based on the density-corrected $V_{\rm max}$ method of
Cole(2011), with three improvements to the original method. To validate the
improved method, we generate two sets of flux-limited samples from two
independent mock catalogs with different $k+e$ corrections. By comparing the
two-point correlation functions, our results demonstrate that the random
catalog created by the smoothed density-corrected $V_{\rm max}$ approach
provides a more accurate and precise measurement for both sets of mock samples
than the commonly used $V_{\rm max}$ method and redshift shuffled method. For
flux-limited samples and color-dependent subsamples, the accuracy of the
projected correlation function is well constrained within $1\%$ on the scale
$0.07 h^{-1}\rm Mpc$ to $30 h^{-1}\rm Mpc$. The accuracy of the redshift-space
correlation function is less than $2\%$ as well. Currently, it is the only
approach that holds promise for achieving the high-accuracy goal of clustering
measures for next-generation surveys.