MAGGIE: Models and Algorithms for Galaxy Groups, Interlopers and Environment

Manuel Duarte, Gary A. Mamon

Published 2014-12-10Version 1

We develop a prior-based, probabilistic, abundance matching (AM) grouping algorithm, MAGGIE, where we combine the previously measured universal distribution of halo interlopers in projected phase space with our knowledge of NFW haloes with realistic internal kinematics. We test MAGGIE-L and MAGGIE-M (in which the AM is performed on the group luminosities and stellar masses, respectively) as well as our previously optimised Friends-of-Friends (FoF) group finder, all for groups of at least 3 galaxies extracted from two subsamples that are doubly complete in distance and luminosity within a mock, flux-limited, SDSS Legacy redshift survey, incorporating (for MAGGIE) reasonable observational errors on galaxy luminosities and stellar masses. Our tests indicate that MAGGIE outperforms the optimal FoF at all log masses (12 to 15 in solar units): groups extracted with MAGGIE are much less likely to be secondary fragments of true groups, and restricting to the primary fragments, the galaxy memberships (relative to the virial sphere of the real-space group) are much more complete and usually more reliable, the MAGGIE AM masses are much less biased and with less scatter, as are the group luminosities and stellar masses (computed in MAGGIE using the membership probabilities as weights). Only for the reliability (purity) of the galaxy population in high-estimated-mass clusters does FoF outperform both implementations of MAGGIE. For the tests in common with other AM group finders, MAGGIE has lower mass scatter than Yang et al., higher galaxy completeness than Yang et al., Mu\~{n}oz-Cuartas & M\"uller and Dom\'{\i}nguez Romero et al., but cannot match the latter's zero mean group fragmentation. The excellent performance of both flavours of MAGGIE should lead to sharper trends of environmental effects on galaxies, as well as more precise mass/orbit modeling of groups of galaxies.