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 (i) the similarity in colour of...

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Published in:Monthly Notices of the Royal Astronomical Society
Main Authors: Murphy, D. N. A., Geach, J. E., Bower, R. G.
Format: Text
Language:English
Published: Oxford University Press 2012
Subjects:
Online Access:http://mnras.oxfordjournals.org/cgi/content/short/420/3/1861
https://doi.org/10.1111/j.1365-2966.2011.19782.x
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spelling fthighwire:oai:open-archive.highwire.org:mnras:420/3/1861 2023-05-15T17:54:00+02:00 ORCA: The Overdense Red-sequence Cluster Algorithm Murphy, D. N. A. Geach, J. E. Bower, R. G. 2012-03-01 00:00:00.0 text/html http://mnras.oxfordjournals.org/cgi/content/short/420/3/1861 https://doi.org/10.1111/j.1365-2966.2011.19782.x en eng Oxford University Press http://mnras.oxfordjournals.org/cgi/content/short/420/3/1861 http://dx.doi.org/10.1111/j.1365-2966.2011.19782.x Copyright (C) 2012, Oxford University Press Papers TEXT 2012 fthighwire https://doi.org/10.1111/j.1365-2966.2011.19782.x 2013-05-28T02:10:31Z 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 ... Text Orca HighWire Press (Stanford University) Stripe ENVELOPE(9.914,9.914,63.019,63.019) Monthly Notices of the Royal Astronomical Society 420 3 1861 1881
institution Open Polar
collection HighWire Press (Stanford University)
op_collection_id fthighwire
language English
topic Papers
spellingShingle Papers
Murphy, D. N. A.
Geach, J. E.
Bower, R. G.
ORCA: The Overdense Red-sequence Cluster Algorithm
topic_facet Papers
description 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 ...
format Text
author Murphy, D. N. A.
Geach, J. E.
Bower, R. G.
author_facet Murphy, D. N. A.
Geach, J. E.
Bower, R. G.
author_sort Murphy, D. N. A.
title ORCA: The Overdense Red-sequence Cluster Algorithm
title_short ORCA: The Overdense Red-sequence Cluster Algorithm
title_full ORCA: The Overdense Red-sequence Cluster Algorithm
title_fullStr ORCA: The Overdense Red-sequence Cluster Algorithm
title_full_unstemmed ORCA: The Overdense Red-sequence Cluster Algorithm
title_sort orca: the overdense red-sequence cluster algorithm
publisher Oxford University Press
publishDate 2012
url http://mnras.oxfordjournals.org/cgi/content/short/420/3/1861
https://doi.org/10.1111/j.1365-2966.2011.19782.x
long_lat ENVELOPE(9.914,9.914,63.019,63.019)
geographic Stripe
geographic_facet Stripe
genre Orca
genre_facet Orca
op_relation http://mnras.oxfordjournals.org/cgi/content/short/420/3/1861
http://dx.doi.org/10.1111/j.1365-2966.2011.19782.x
op_rights Copyright (C) 2012, Oxford University Press
op_doi https://doi.org/10.1111/j.1365-2966.2011.19782.x
container_title Monthly Notices of the Royal Astronomical Society
container_volume 420
container_issue 3
container_start_page 1861
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