迈向依赖属性的星系成团性的精确测量：II. 平滑密度校正 $V_{\ m max}$ 方法的检验
Toward accurate measurement of property-dependent galaxy clustering: II. Tests of the smoothed density-corrected $V_{\rm max}$ method
AI总结：
本文改进 Cole(2011) 的密度校正 $V_{\ m max}$ 方法，提出平滑密度校正随机星表构建技术，并在模拟样本中证明其显著提升依赖属性的星系成团性测量精度。
摘要：
我们提出了一种平滑密度校正的 $V_{\ m max}$ 技术，用于构建随机星表，以估计依赖属性的星系成团性。该方法本质上基于 Cole(2011) 的密度校正 $V_{\ m max}$ 方法，并对原始方法作了三项改进。为验证改进后的方法，我们由两个具有不同 $k+e$ 改正的独立模拟星表生成了两组流量限定样本。通过比较两点相关函数，我们的结果表明：与常用的 $V_{\ m max}$ 方法和红移重排方法相比，由平滑密度校正 $V_{\ m max}$ 方法创建的随机星表对两组模拟样本均给出更准确、更精确的测量。对于流量限定样本和依赖颜色的子样本，在 $0.07 h^{-1}\ m Mpc$ 至 $30 h^{-1}\ m Mpc$ 的尺度上，投影相关函数的精度被很好地限制在 $1\%$ 以内；红移空间相关函数的精度也优于 $2\%$。目前，这是唯一有望实现下一代巡天高精度成团性测量目标的方法。
英文摘要：
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 for the projected correlation function is well constrained within $1\%$ on the scale $0.07 h^{-1}\rm Mpc - 30 h^{-1}\rm Mpc$. 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 goal of high-accuracy clustering measures for next-generation surveys.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
