orca: The Overdense Red-sequence Cluster Algorithm

Authors

  • D. N. A. Murphy,

    Corresponding author
    1. Institute for Computational Cosmology, Durham University, South Road, Durham DH1 3LE
      E-mail: david.murphy@durham.ac.uk
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  • J. E. Geach,

    1. Institute for Computational Cosmology, Durham University, South Road, Durham DH1 3LE
    2. Department of Physics, McGill University, Ernest Rutherford Building, 3600 Rue University, Montréal, Québec H3A 2T8, Canada
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  • R. G. Bower

    1. Institute for Computational Cosmology, Durham University, South Road, Durham DH1 3LE
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E-mail: david.murphy@durham.ac.uk

ABSTRACT

We present a new cluster-detection algorithm designed for the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) survey but with generic application to any multiband data. The method makes no prior assumptions about the properties of clusters other than (i) the similarity in colour of cluster galaxies (the ‘red sequence’); and (ii) an enhanced projected surface density. The detector has three main steps: (i) it identifies cluster members by photometrically filtering the input catalogue to isolate galaxies in colour–magnitude space; (ii) a Voronoi diagram identifies regions of high surface density; and (iii) galaxies are grouped into clusters with a Friends-of-Friends technique. Where multiple colours are available, we require systems to exhibit sequences in two colours. In this paper, we present the algorithm and demonstrate it on two data sets. The first is a 7-deg2 sample of the deep Sloan Digital Sky Survey (SDSS) equatorial stripe (Stripe 82), from which we detect 97 clusters with z≤ 0.6. Benefitting from deeper data, we are 100 per cent complete in the maxBCG optically selected cluster catalogue (based on shallower single-epoch SDSS data) and find an additional 78 previously unidentified clusters. The second data set is a mock Medium Deep Survey Pan-STARRS catalogue, based on the Λ cold dark matter (ΛCDM) model and a semi-analytic galaxy formation recipe. Knowledge of galaxy–halo memberships in the mock catalogue allows for the quantification of algorithm performance. We detect 305 mock clusters in haloes with mass >1013 h−1 M at z≲ 0.6 and determine a spurious detection rate of <1 per cent, consistent with tests on the Stripe 82 catalogue. The detector performs well in the recovery of model ΛCDM clusters. At the median redshift of the catalogue, the algorithm achieves >75 per cent completeness down to halo masses of 1013.4 h−1 M and recovers >75 per cent of the total stellar mass of clusters in haloes down to 1013.8 h−1 M. A companion paper presents the complete cluster catalogue over the full 270-deg2 Stripe 82 catalogue.

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