A 3D Voronoi+Gapper Galaxy Cluster Finder in Redshift Space to z ∼ 0.2 I: an Algorithm Optimized for the 2dFGRS

Pereira, Sebastián; Campusano, Luis E.; Hitschfeld, Nancy; Pizarro, Daniel; Haines, C. P.; Clowes, R. G.; Marinello, Gabriel; Söchting, Ilona K.

doi:10.3847/1538-4357/838/2/109

Cited by 10 publications

(6 citation statements)

References 63 publications

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“…There are several methods to extract groups of galaxy catalogues: Friends-Of-Friends (FOF; Huchra & Geller 1982), Halo-based (Yang et al 2005), Voronoi-Delaunay-based (Gerke et al 2012;Pereira et al 2017), matched filter (Kepner et al 1999;Milkeraitis et al 2010), density field-based (Miller et al 2005;Sharma & Johnston 2009;Smith et al 2012), and Bayesian-based using the marked point process framework (Tempel et al 2018), among others. In this work, we focus our attention on improving the redshift space identification of groups obtained by the two most used techniques at this time: FOF and Halo-based.…”

Section: Introductionmentioning

confidence: 99%

Combining friend-of-friend and halo-based algorithms for the identification of galaxy groups

Rodríguez

Merchán

2020

A&A

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Context. Galaxy groups provide the means for a great diversity of studies that contribute to a better understanding of the structure of the universe on a large scale and allow the properties of galaxies to be linked to those of the host halos. However, the identification of galaxy systems is a challenging task and therefore it is necessary to improve the techniques involved as much as possible. Aims. In view of the large present and forthcoming galaxy catalogues, we propose, implement, and evaluate an algorithm that combines the two most popular techniques used to identify galaxy systems. The algorithm can be easily applied to any spectroscopic galaxy catalogue, but here we demonstrate its use on the Sloan Digital Sky Survey. Methods. Assuming that a galaxy group is a gravitationally bounded system that has at least one bright galaxy, we begin by identifying groups with a Friends-Of-Friends algorithm adapted to fit this definition. In view of the shortcomings of this method, particularly the lack of ability to identify low-number groups, and consequently the inability to study the occupation of halos throughout the mass range, we improve it by adding a halo-based procedure. To assess the performance, we construct a mock catalogue from a semianalytical model to compare the groups identified using our method with those obtained from the simulation. Results. The comparison of groups extracted using our method with those of a mock catalogue shows that the proposed algorithm provides excellent results. The modifications introduced to the Friends-Of-Friends algorithm in the first part of the procedure to fit the adopted group definition gave reliable groups. Furthermore, incorporation of the halo-based method reduces the interlopers while more accurately reproducing the number of galaxies per group. As a specific application, we use the algorithm to extract groups from the Sloan Digital Sky Survey.

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Section: Introductionmentioning

confidence: 99%

Combining friend-of-friend and halo-based algorithms for the identification of galaxy groups

Rodríguez

Merchán

2020

A&A

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“…This red-sequence-based technique assumes the existence of a tight red sequence for clusters, and uses only quiescent galaxies as a proxy of their host cluster environment. There are other cluster finding methods which are used in the literature, including density-field based methods (e.g., Miller et al 2005), matched filter techniques (e.g., Kepner et al 1999;Milkeraitis et al 2010;Bellagamba et al 2018), and the Voronoi-Delaunay method (e.g., Ramella et al 2001;Pereira et al 2017;Soares-Santos et al 2011). These methods are capable of identifying clusters and groups of different richness ranging from a pair of galaxies to very massive clusters with hundreds of galaxies for entire surveys.…”

Section: Introductionmentioning

confidence: 99%

GalWeight Application: A Publicly Available Catalog of Dynamical Parameters of 1800 Galaxy Clusters from SDSS-DR13, (GalWCat19)

Abdullah

Wilson

Klypin

et al. 2019

ApJS

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Utilizing the SDSS-DR13 spectroscopic dataset, we create a new publicly-available catalog of 1,800 galaxy clusters (GalWeight cluster catalog, GalWCat19) and a corresponding catalog of 34,471 identified member galaxies. The clusters are identified from overdensities in redshift-phase space. The GalWeight technique introduced in Abdullah, Wilson and Klypin (AWK18) is then applied to identify cluster members. The completeness of the cluster catalog (GalWCat19) and the procedure followed to determine cluster mass are tested on the Bolshoi N-body simulations. The 1,800 GalWCat19 clusters range in redshift between 0.01 − 0.2 and in mass between (0.4 − 14) × 10 14 h −1 M . The cluster catalog provides a large number of cluster parameters including sky position, redshift, membership, velocity dispersion, and mass at overdensities ∆ = 500, 200, 100, 5.5. The 34,471 member galaxies are identified within the radius at which the density is 200 times the critical density of the Universe. The galaxy catalog provides the coordinates of each galaxy and the ID of the cluster that the galaxy belongs to. The cluster velocity dispersion scales with mass as log(σ 200 ) = log(946±52 km s −1 )+(0.349±0.142) log h(z) M 200 /10 15 M with scatter of δ log σ = 0.06. The catalogs are publicly available at the following website a .

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“…The idea of these methods is to use the underlying cosmological model and to group together galaxies that belong to the same dark matter halo. In addition to the FoF and halo-based group finders, there are other group finders that are occasionally used, including the Voronoi-Delaunay method (Ramella et al 2001;Gerke et al 2005Gerke et al , 2012Cucciati et al 2010;Pereira et al 2017), matched filter techniques (Kepner et al 1999;Milkeraitis et al 2010;Bellagamba et al 2018) and density field based methods (Miller et al 2005;Sharma & Johnston 2009;Smith et al 2012). Abdullah et al (2018) propose a weighting technique to determine galaxy group and cluster membership.…”

Section: Introductionmentioning

confidence: 99%

Bayesian group finder based on marked point processes

Tempel

Kruuse

Kipper

et al. 2018

A&A

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Context. Galaxy groups and clusters are formidable cosmological probes. They permit the studying of the environmental effects on galaxy formation. A reliable detection of galaxy groups is an open problem and is important for ongoing and future cosmological surveys. Aims. We propose a probabilistic galaxy group detection algorithm based on marked point processes with interactions. Methods. The pattern of galaxy groups in a catalogue is seen as a random set of interacting objects. The positions and the interactions of these objects are governed by a probability density. The parameters of the probability density were chosen using a priori knowledge. The estimator of the unknown cluster pattern is given by the configuration of objects maximising the proposed probability density. Adopting the Bayesian framework, the proposed probability density is maximised using a simulated annealing (SA) algorithm. At fixed temperature, the SA algorithm is a Monte Carlo sampler of the probability density. Hence, the method provides “for free” additional information such as the probabilities that a point or two points in the observation domain belong to the cluster pattern, respectively. These supplementary tools allow the construction of tests and techniques to validate and to refine the detection result. Results. To test the feasibility of the proposed methodology, we applied it to the well-studied 2MRS data set. Compared to previously published Friends-of-Friends (FoF) group finders, the proposed Bayesian group finder gives overall similar results. However for specific applications, like the reconstruction of the local Universe, the details of the grouping algorithms are important. Conclusions. The proposed Bayesian group finder is tested on a galaxy redshift survey, but more detailed analyses are needed to understand the actual capabilities of the algorithm regarding upcoming cosmological surveys. The presented mathematical framework permits adapting it easily for other data sets (in astronomy and in other fields of sciences). In cosmology, one promising application is the detection of galaxy groups in photometric galaxy redshift surveys, while taking into account the full photometric redshift posteriors.

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A 3D Voronoi+Gapper Galaxy Cluster Finder in Redshift Space to z ∼ 0.2 I: an Algorithm Optimized for the 2dFGRS

Cited by 10 publications

References 63 publications

Combining friend-of-friend and halo-based algorithms for the identification of galaxy groups

Combining friend-of-friend and halo-based algorithms for the identification of galaxy groups

GalWeight Application: A Publicly Available Catalog of Dynamical Parameters of 1800 Galaxy Clusters from SDSS-DR13, (GalWCat19)

Bayesian group finder based on marked point processes

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