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