Probability Friends-of-Friends (PFOF) Group Finder: Performance Study and Observational Data Applications on Photometric Surveys

(Abridged) In tandem with observational datasets, we utilize realistic mock catalogs, based on a semi-analytic galaxy formation model, constructed specifically for Pan-STARRS1 Medium Deep Surveys in order to assess the performance of the Probability Friends-of-Friends (PFOF, Liu et al.) 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 ({sigma}{Delta}z/(1+zs)), we find that catalog purities and completenesses from ``good {sigma}{Delta}z/(1+zs)) ~ 0.01) to ``poor {sigma}{Delta}z/(1+zs)) ~ 0.07) photo-zs gradually degrade respectively from 77% and 70% to 52% and 47%. To avoid model dependency of the mock for use on observational data we apply a ``subset optimization approach, using spectroscopic-redshift group data from the target field to train the group finder for application to that field, as an alternative method for the grouping optimization. We demonstrate this approach using these spectroscopically identified groups as the training set, i.e. zCOSMOS groups for PFOF searches within PS1 Medium Deep Field04 (PS1MD04) and DEEP2 EGS groups for searches in PS1MD07. We ultimately apply PFOF to four datasets spanning the photo-z uncertainty range from 0.01 to 0.06 in order to quantify the dependence of group recovery performance on photo-z accuracy. We find purities and completenesses calculated from observational datasets broadly agree with their mock analogues. Further tests of the PFOF algorithm are performed via matches to X-ray clusters identified within the PS1MD04 and COSMOS footprints. Across over a decade in group mass, we find PFOF groups match ~85% of X-ray clusters in COSMOS and PS1MD04, but at a lower statistical significance in the latter.