自适应匹配滤波光学寻迹算法及其仿真比较
Optical Cluster-Finding with An Adaptive Matched-Filter Technique: Algorithm and Comparison with Simulations
摘要：
我们提出了一种改进的自适应匹配滤波算法，旨在确定星系团在宽视场成像调查，如斯隆数字巡天。集群发现技术是完全自适应的成像调查与光谱覆盖范围，测光红移，没有红移的信息，以及任何组合，这些在一个调查。它在多波段成像测量中具有高效率，其中光度红移可以用很好理解的误差分布来估计。在真实的模拟SDSS星表上对算法的测试表明，对于质量大于1.0*10^{14} h^{-1} M_solar且红移达到z=0.45的星系团，检测到的样本完整度约为85%，纯度超过90%。用极大似然方法估算的星系团红移的误差在整个红移范围内都很小（一般小于0.01），光度红移误差与Sloan巡天观测中发现的结果相似。在对应于Delta=200的星系过密度的球形半径内，我们发现导出的星系团丰富度Lambda_{200}是其维里质量M_{200}的大致线性指标，这很好地恢复了输入模拟的总光度和星系团质量之间的关系。
英文摘要：
We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multi-band imaging surveys where photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is ~85% complete and over 90% pure for clusters with masses above 1.0*10^{14} h^{-1} M_solar and redshifts up to z=0.45. The errors of estimated cluster redshifts from maximum likelihood method are shown to be small (typically less that 0.01) over the whole redshift range with photometric redshift errors typical of those found in the Sloan survey. Inside the spherical radius corresponding to a galaxy overdensity of Delta=200, we find the derived cluster richness Lambda_{200} a roughly linear indicator of its virial mass M_{200}, which well recovers the relation between total luminosity and cluster mass of the input simulation.
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。
