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...
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ftdatacite:10.48550/arxiv.1109.3182 2023-05-15T17:53:39+02:00 ORCA: The Overdense Red-sequence Cluster Algorithm Murphy, D. N. A. Geach, J. E Bower, R. G. 2011 https://dx.doi.org/10.48550/arxiv.1109.3182 https://arxiv.org/abs/1109.3182 unknown arXiv https://dx.doi.org/10.1111/j.1365-2966.2011.19782.x arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Cosmology and Nongalactic Astrophysics astro-ph.CO FOS Physical sciences article-journal Article ScholarlyArticle Text 2011 ftdatacite https://doi.org/10.48550/arxiv.1109.3182 https://doi.org/10.1111/j.1365-2966.2011.19782.x 2022-04-01T13:54:59Z 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) : 22 pages, 17 figures. Accepted for publication in MNRAS. ORCA cluster catalogues available at http://orca.dur.ac.uk/ Text Orca DataCite Metadata Store (German National Library of Science and Technology) Lambda ENVELOPE(-62.983,-62.983,-64.300,-64.300) Stripe ENVELOPE(9.914,9.914,63.019,63.019) |
institution |
Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Cosmology and Nongalactic Astrophysics astro-ph.CO FOS Physical sciences |
spellingShingle |
Cosmology and Nongalactic Astrophysics astro-ph.CO FOS Physical sciences Murphy, D. N. A. Geach, J. E Bower, R. G. ORCA: The Overdense Red-sequence Cluster Algorithm |
topic_facet |
Cosmology and Nongalactic Astrophysics astro-ph.CO FOS Physical sciences |
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 (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) : 22 pages, 17 figures. Accepted for publication in MNRAS. ORCA cluster catalogues available at http://orca.dur.ac.uk/ |
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 |
arXiv |
publishDate |
2011 |
url |
https://dx.doi.org/10.48550/arxiv.1109.3182 https://arxiv.org/abs/1109.3182 |
long_lat |
ENVELOPE(-62.983,-62.983,-64.300,-64.300) ENVELOPE(9.914,9.914,63.019,63.019) |
geographic |
Lambda Stripe |
geographic_facet |
Lambda Stripe |
genre |
Orca |
genre_facet |
Orca |
op_relation |
https://dx.doi.org/10.1111/j.1365-2966.2011.19782.x |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1109.3182 https://doi.org/10.1111/j.1365-2966.2011.19782.x |
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1766161354925801472 |