A new algorithm is developed, based on the fnends-of-fhends (FOF) algorithm, to identify galaxy groups in a galaxy catalog in which the redshift errors have large dispersions (e.g., a photometric redshift galaxy catalog in which a portion of the galaxies also have much more precise spectroscopic redshifts). The DEEP2 mock catalogs, with our additional simulated photometric redshift errors, are used to test the performance of our algorithm. The association of the reconstructed galaxy groups with the dark halos in the mock catalogs gives an idea about the completeness and purity of the derived group catalog. Our results show that in a 0.6 ≤ z ≤ 1.6 galaxy catalog with an R-band limiting magnitude of 24.1 and an average 1σ photometric redshift error of ～0.03, the overall purity of our new algorithm for richness 4-7 (line-of-sight velocity dispersion ～300 km s~(-1)) groups is higher than 70% (i.e., 70% of the groups reconstructed by our algorithm are related to real galaxy groups). The performance of the new algorithm is compared with the performance of the FOF algorithm, and it is suggested that this new algorithm is better than FOF for a database, given the same redshift uncertainties.
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机译：基于funends-fhends（FOF）算法，开发了一种新算法，以识别红移误差具有较大色散的星系目录中的星系组（例如，其中一部分星系的光度红移星系目录）也有更精确的光谱红移）。 DEEP2模拟目录以及我们额外的模拟光度红移误差用于测试算法的性能。模拟目录中重建的星系组与黑暗光环的关联给出了有关派生组目录的完整性和纯度的想法。我们的结果表明，在0.6≤z≤1.6的星系目录中，R波段的极限强度为24.1，平均1σ光度红移误差为〜0.03，我们新的富度算法的整体纯度为4-7（视线速度色散〜300 km s〜（-1））组高于70％（即，通过我们的算法重建的组中有70％与真实星系组有关）。将新算法的性能与FOF算法的性能进行了比较，并建议在给定相同的红移不确定性的情况下，该新算法优于数据库的FOF。
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