Finding Galaxy Groups In Photometric Redshift Space: the Probability Friends-of-Friends (pFoF) Algorithm

TL;DR

The paper introduces the pFoF algorithm, which combines friends-of-friends and photometric redshift probability densities to effectively identify galaxy groups in photometric data, improving redshift estimates and detection accuracy.

Contribution

The novel pFoF algorithm integrates transverse friends-of-friends with photometric redshift probabilities, enhancing galaxy group detection in photometric surveys.

Findings

Recovery rate exceeds 80% for groups with mass > 2×10^13 M_sun

False detection rate is about 10% for groups with at least 8 members

Results are consistent with mock catalog tests and real data applications

Abstract

We present a structure finding algorithm designed to identify galaxy groups in photometric redshift data sets: the probability friends-of-friends (pFoF) algorithm. This algorithm is derived by combining the friends-of-friends algorithm in the transverse direction and the photometric redshift probability densities in the radial dimension. The innovative characteristic of our group-finding algorithm is the improvement of redshift estimation via the constraints given by the transversely connected galaxies in a group, based on the assumption that all galaxies in a group have the same redshift. Tests using the Virgo Consortium Millennium Simulation mock catalogs allow us to show that the recovery rate of the pFoF algorithm is larger than 80% for mock groups of at least $2\times10^{13}M_{\sun}$, while the false detection rate is about 10% for pFoF groups containing at least $\sim8$ net members. Applying the algorithm to the CNOC2 group catalogs gives results which are consistent with the mock catalog tests. From all these results, we conclude that our group-finding algorithm offers an effective yet simple way to identify galaxy groups in photometric redshift catalogs.