Probability friends-of-friends (PFOF) group finder : performance study and observational data applications on photometric surveys.

In tandem with observational data sets, we utilize realistic mock catalogs, based on a semi-analytic galaxy formation model, constructed specifically for Pan-STARRS1 Medium Deep Surveys to assess the performance of the Probability Friends-of-Friends (PFOF) group finder, and aim to develop a grouping optimization method applicable to surveys like Pan-STARRS1. Producing mock PFOF group catalogs under a variety of photometric redshift accuracies (), we find that catalog purities and completenesses from "good" ( 0.01) to "poor" ( 0.07) photo-zs gradually degrade from 77% and 70% to 52% and 47%, respectively. A "subset optimization" approach is developed by using spectroscopic-redshift group data from the target field to train the group finder for application to that field and demonstrated using zCOSMOS groups for PFOF searches within PS1 Medium Deep Field04 (PS1MD04) and DEEP2 EGS groups in PS1MD07. With four data sets spanning the photo-z accuracy range from 0.01 to 0.06, we find purities and completenesses agree with their mock analogs. Further tests are performed via matches to X-ray clusters. We find PFOF groups match ∼85% of X-ray clusters identified in COSMOS and PS1MD04, lending additional support to the reliability of the detection algorithm. In the end, we demonstrate, by separating red and blue group galaxies in the EGS and PS1MD07 group catalogs, that the algorithm is not biased with respect to specifically recovering galaxies by color. The analyses suggest the PFOF algorithm shows great promise as a reliable group finder for photometric galaxy surveys of varying depth and coverage. © 2014. The American Astronomical Society. All rights reserved..