ORCA: The Overdense Red-sequence Cluster Algorithm
  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 (a) the similarity in colour of cluster
galaxies (the "red sequence") and (b) 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, (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 datasets. The first is a 7 square degree
sample of the deep Sloan Digital Sky Survey equatorial stripe (Stripe 82), from
which we detect 97 clusters with z<=0.6. Benefiting from deeper data, we are
100% 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 dataset is a mock Medium Deep Survey (MDS)
Pan-STARRS catalogue, based on the Lambda-CDM model and a semi-analytic galaxy
formation recipe. Knowledge of galaxy-halo memberships in the mock allows a
quantification of algorithm performance. We detect 305 mock clusters in haloes
with mass >10^13 solar masses at z<=0.6 and determine a spurious detection rate
of <1%, consistent with tests on the Stripe 82 catalogue. The detector performs
well in the recovery of model Lambda-CDM clusters. (abridged)
