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

¹ Instituto de Astronomía Teórica y Experimental, IATE, CONICET, Laprida 854, X5000BGR Córdoba, Argentina
e-mail: arielz77@gmail.com
² Observatorio Astronómico, Universidad Nacional de Córdoba, Laprida 854, X5000 BGR Córdoba, Argentina
³ Instituto de Astronomía, Geofísica e Ciencias Atmosfericas, IAG, USP, 1226 rua do Matão, São Paulo, Brazil
⁴ Instituto de Astrofisica de Andalucía (CSIC), Apdo. 3044, 18008 Granada, Spain
⁵ University of Michigan, Ann Arbor MI 48109, USA; Eureka Scientific Inc., Oakland CA 94602-3017, USA
⁶ Observatório Nacional, rua Gal. José Cristino, 20921-400 Rio de Janeiro, Brazil
⁷ Department of Physics and Astronomy, University of Alabama, Box 870324, Tuscaloosa AL 35487, USA

Received: 4 October 2013
Accepted: 4 November 2013

Abstract

Aims. We have performed a detailed analysis of the ability of the friends-of-friends algorithm to identify real galaxy systems in deep surveys such as the future Javalambre Physics of the Accelerating Universe Astrophysical Survey. Our approach was twofold: assessing the reliability of the algorithm in real and in redshift space. In the latter, our intention was also to determine the degree of accuracy that could be achieved when using spectroscopic or photometric-redshift determinations as a distance indicator.

Methods. We 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 explored different ways to define the proper linking length parameters of the algorithm to identify the best-suited galaxy groups in each case.

Results. We found that when one identifies 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 we tested the purity and completeness of the group samples, we found that the best resulting group sample reaches values of ~40% and ~70% of systems with high levels of purity and completeness, when spectroscopic information was used. To identify 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 fewer than ~40% false identifications at the same time as we recover around 60% of the true groups.

Conclusions. 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 made with the data.