AMICO: optimized detection of galaxy clusters in photometric surveys
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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.Abstract:
We present AMICO (Adaptive Matched Identifier of Clustered Objects), a new algorithm for the detection of galaxy clusters in photometric surveys. AMICO is based on the Optimal Filtering technique, which allows to maximise the signal-to-noise ratio of the clusters. In this work we focus on the new iterative approach to the extraction of cluster candidates from the map produced by the filter. In particular, we provide a definition of membership probability for the galaxies close to any cluster candidate, which allows us to remove its imprint from the map, allowing the detection of smaller structures. As demonstrated in our tests, this method allows the deblending of close-by and aligned structures in more than $50\%$ of the cases for objects at radial distance equal to $0.5 \times R_{200}$ or redshift distance equal to $2 \times \sigma_z$, being $\sigma_z$ the typical uncertainty of photometric redshifts. Running AMICO on mocks derived from N-body simulations and semi-analytical modelling of the galaxy evolution, we obtain a consistent mass-amplitude relation through the redshift range $0.3 5.read more
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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
Modelling projection effects in optically selected cluster catalogues
M. Costanzi,Eduardo Rozo,Eli S. Rykoff,Eli S. Rykoff,Arya Farahi,Tesla E. Jeltema,A. E. Evrard,Adam Mantz,Daniel Gruen,Daniel Gruen,Rachel Mandelbaum,J. DeRose,T. McClintock,T. N. Varga,T. N. Varga,Yanxi Zhang,Yanxi Zhang,Jochen Weller,Jochen Weller,Risa H. Wechsler,Risa H. Wechsler,Michel Aguena +21 more
TL;DR: In this paper, the authors developed an empirical method to characterize the impact of projection effects on redMaPPer cluster catalogues and used numerical simulations to validate their method and illustrate its robustness.
Journal ArticleDOI
amico galaxy clusters in KiDS-DR3: sample properties and selection function
Matteo Maturi,F. Bellagamba,F. Bellagamba,Mario Radovich,Mauro Roncarelli,Mauro Roncarelli,Mauro Sereno,Mauro Sereno,Lauro Moscardini,Lauro Moscardini,S. Bardelli,E. Puddu +11 more
TL;DR: In this paper, the authors presented the first catalogue of galaxy cluster candidates derived from the third data release of the Kilo Degree Survey (KiDS-DR3) using the Adaptive Matched Identifier of Clustered Objects (AMICO) algorithm.
Journal ArticleDOI
Improving the open cluster census - I. Comparison of clustering algorithms applied to Gaia DR2 data
Emily L. Hunt,Sabine Reffert +1 more
TL;DR: A comparison of clustering algorithms used to detect open clusters is conducted, attempting to statistically quantify their strengths and weaknesses by deriving the sensitivity, specificity, and precision of each as well as their true positive rate against a larger sample.
Journal ArticleDOI
AMICO galaxy clusters in KiDS-DR3: weak lensing mass calibration
F. Bellagamba,F. Bellagamba,Mauro Sereno,Mauro Sereno,Mauro Roncarelli,Mauro Roncarelli,Matteo Maturi,Mario Radovich,S. Bardelli,E. Puddu,Lauro Moscardini,Lauro Moscardini,Fedor Getman,Hendrik Hildebrandt,Nicola R. Napolitano,Nicola R. Napolitano +15 more
TL;DR: In this paper, the mass calibration for galaxy clusters detected with the AMICO code in KiDS DR3 data is presented, where the authors perform a weak lensing stacked analysis by binning the clusters according to redshift and two different mass proxies provided by AMICO, namely the amplitude $A$ (measure of galaxy abundance through an optimal filter) and the richness $\lambda^*$ (sum of membership probabilities in a consistent radial and magnitude range across redshift).
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