Assessing the reliability of friends-of-friends groups on the future Javalambre Physics of the Accelerating Universe Astrophysical Survey

ARIEL ZANDIVAREZ, EUGENIA DÍAZ-GIMENEZ, CLAUDIA MENDES DE OLIVEIRA, BEGOÑA ASCASO, NARCISO BENÍTEZ, RENATO DUPKE, LAERTE SODRÉ JR., JIMMY IRWIN

ASTRONOMY AND ASTROPHYSICS

EDP SCIENCES S A

Lugar: Paris; Año: 2014 vol. 561 p. 71 - 91

0004-6361

We have performed a detailed analysis of the ability of the friends-of-friends algorithm in identifying real galaxy systems in deep surveys such as the future Javalambre Physics of the Accelerating Universe Astrophysical Survey. Our approach is two-fold, i.e., assessing the reliability of the algorithm in both real and redshift space. In the latter, our intention is also to determine the degree of accuracy that could be achieved when using spectroscopic or photometric redshift determinations as a distance indicator. We have built a light-cone mock catalogue using synthetic galaxies constructed from the Millennium Run Simulation I plus a semi-analytical model of galaxy formation. We have explored different ways to define the proper linking length parameters of the algorithm in order to perform an identification of galaxy groups as suitable as possible in each case. We find that, when identifying systems in redshift space using spectroscopic information, the linking lengths should take into account the variation of the luminosity function with redshift as well as the linear redshift dependence of the radial fiducial velocity in the line of sight direction. When testing purity and completeness of the group samples, we find that the best resulting group sample reaches values of 40% and 70% of systems with high levels of purity and completeness, respectively, when using spectroscopic information. When identifying systems using photometric redshifts, we adopted a probabilistic approach to link galaxies in the line of sight direction. Our result suggests that it is possible to identify a sample of groups with less than 40% false identification at the same time as we recover around 60% of the true groups. This modified version of the algorithm can be applied to deep surveys provided that the linking lengths are selected appropriately for the science to be done with the data.