Bayesian Cluster Finder: Clusters in the CFHTLS Archive Research Survey
TLDR
In this paper, the authors presented a new technique for detecting galaxy clusters based on the Matched Filter Algorithm from a Bayesian point of view, which is able to determine the position, redshift and richness of the cluster through the maximization of a filter depending on galaxy luminosity, density and photometric redshift combined with a galaxy cluster prior that accounts for color-magnitude relations and BCG-redshift relation.Abstract:
The detection of galaxy clusters in present and future surveys enables measuring mass-to-light ratios, clustering properties, galaxy cluster abundances and therefore, constraining cosmological parameters. We present a new technique for detecting galaxy clusters, which is based on the Matched Filter Algorithm from a Bayesian point of view. The method is able to determine the position, redshift and richness of the cluster through the maximization of a filter depending on galaxy luminosity, density and photometric redshift combined with a galaxy cluster prior that accounts for color-magnitude relations and BCG-redshift relation. We tested the algorithm through realistic mock galaxy catalogs, revealing that the detections are 100% complete and 80% pure for clusters up to z $ $20 (Abell Richness $\sim$0, M$\sim4\times10^{14} M_{\odot}$). The completeness and purity remains approximately the same if we do not include the prior information, implying that this method is able to detect galaxy cluster with and without a well defined red sequence. We applied the algorithm to the CFHTLS Archive Research Survey (CARS) data, recovering similar detections as previously published using the same or deeper data plus additional clusters which appear to be real.read more
Citations
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The Evolving Luminosity Function of Red Galaxies
Buell T. Jannuzi,Michael J. I. Brown,Arjun Dey,K. Brand,Andrew J. Benson,Mark Brodwin,Darren J. Croton,Peter Eisenhardt +7 more
TL;DR: In this paper, the authors trace the assembly history of red galaxies since z = 1 by measuring their evolving space density with the B-band luminosity function, which is consistent with star-forming galaxies being transformed into L* red galaxies after a decline in their star formation rates.
Journal ArticleDOI
The redMaPPer Galaxy Cluster Catalog From DES Science Verification Data
Eli S. Rykoff,Eduardo Rozo,Devon L. Hollowood,A. Bermeo-Hernandez,Tesla E. Jeltema,Julian A. Mayers,A. K. Romer,P. Rooney,A. Saro,C. Vergara Cervantes,Risa H. Wechsler,H. Wilcox,T. M. C. Abbott,F. B. Abdalla,F. B. Abdalla,S. Allam,J. Annis,A. Benoit-Lévy,A. Benoit-Lévy,Gary Bernstein,E. Bertin,David Brooks,D. L. Burke,Diego Capozzi,A. Carnero Rosell,M. Carrasco Kind,Francisco J. Castander,M. Childress,Chris A. Collins,Carlos E. Cunha,C. B. D'Andrea,C. B. D'Andrea,L. N. da Costa,Tamara M. Davis,Shantanu Desai,Shantanu Desai,H. T. Diehl,J. P. Dietrich,J. P. Dietrich,Peter Doel,August E. Evrard,D. A. Finley,B. Flaugher,Pablo Fosalba,Joshua A. Frieman,Karl Glazebrook,Daniel A. Goldstein,Daniel A. Goldstein,Daniel Gruen,Robert A. Gruendl,G. Gutierrez,Matt Hilton,K. Honscheid,Ben Hoyle,David J. James,Scott T. Kay,Kyler Kuehn,N. Kuropatkin,Ofer Lahav,Geraint F. Lewis,C. Lidman,Marcos Lima,M. A. G. Maia,Robert G. Mann,Jennifer L. Marshall,Paul Martini,Peter Melchior,Christopher J. Miller,Ramon Miquel,Joseph J. Mohr,Robert C. Nichol,Brian Nord,Ricardo L. C. Ogando,A. A. Plazas,Kevin Reil,Martin Sahlén,E. J. Sanchez,Basilio X. Santiago,V. Scarpine,Michael Schubnell,I. Sevilla-Noarbe,R. C. Smith,Marcelle Soares-Santos,Flavia Sobreira,John P. Stott,E. Suchyta,M. E. C. Swanson,Gregory Tarle,Daniel Thomas,Douglas L. Tucker,Syed Uddin,Pedro T. P. Viana,V. Vikram,Alistair R. Walker,Yanming Zhang +94 more
TL;DR: In this paper, the authors describe updates to the Redmapper{} algorithm, a photometric red-sequence cluster finder specifically designed for large photometric surveys, applied to data from the Dark Energy Survey (DES), and to the Sloan Digital Sky Survey (SDSS) DR8 photometric data set.
Journal ArticleDOI
Galaxy Cluster Mass Reconstruction Project: I. Methods and first results on galaxy-based techniques
Lyndsay Old,Ramin A. Skibba,Frazer R. Pearce,Darren J. Croton,Stuart I. Muldrew,J. C. Muñoz-Cuartas,Daniel Gifford,Meghan E. Gray,A. von der Linden,A. von der Linden,Gary A. Mamon,Michael R. Merrifield,Volker Müller,R. J. Pearson,Trevor J. Ponman,Alexandro Saro,T. Sepp,Cristóbal Sifón,Elmo Tempel,Elmo Tempel,E. Tundo,Yang Wang,Yang Wang,Yang Wang,Radosław Wojtak +24 more
TL;DR: In this paper, a comparison of various galaxy-based cluster mass estimation techniques that utilize the positions, velocities and colours of galaxies is presented. But, the authors do not evaluate the performance of these methods on a diverse set of models that will increase in complexity.
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An automatic taxonomy of galaxy morphology using unsupervised machine learning
TL;DR: An unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data is presented and a good level of concordance between human and machine classifications is demonstrated.
Journal ArticleDOI
AMICO: optimized detection of galaxy clusters in photometric surveys
TL;DR: AMICO as discussed by the authors is based on the Optimal Filtering technique, which allows to maximise the signal-to-noise ratio of the clusters, and provides a definition of membership probability for the galaxies close to any cluster candidate, allowing the detection of smaller structures.
References
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CARS: the CFHTLS-Archive-Research Survey - I. Five-band multi-colour data from 37 sq. deg. CFHTLS-wide observations
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Journal ArticleDOI
The Cut-and-Enhance Method: Selecting Clusters of Galaxies from the Sloan Digital Sky Survey Commissioning Data
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TL;DR: The cut-and-enhance (CE) method as discussed by the authors uses simple color cuts, combined with a density enhancement algorithm, to up-weight pairs of galaxies that are close in both angular separation and color.
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