Galaxy Cluster Mass Reconstruction Project – IV. Understanding the effects of imperfect membership on cluster mass estimation

Wojtak, Radosław; Old, Lyndsay; Mamon, G. A.; Pearce, Frazer R.; Carvalho, R. R. de; Sifón, Cristobál; Gray, Meghan E.; Skibba, Ramin; Croton, Darren J.; Bamford, Steven P.; Gifford, Daniel; Linden, Anja von der; Muñoz-Cuartas, J. C.; Müller, V.; Pearson, Richard; Rozo, Eduardo; Rykoff, E. S.; Saro, A.; Sepp, T.; Tempel, Elmo

doi:10.1093/mnras/sty2257

Cited by 40 publications

(37 citation statements)

References 60 publications

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“…Majority of the large groups and clusters are located in the bottom left corner with low contamination and incompleteness. This figure should be compared with results presented in Wojtak et al (2018). Right panel: cumulative distribution of contamination and incompleteness for groups shown in the left panel.…”

Section: Discussionmentioning

confidence: 95%

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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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Section: Discussionmentioning

confidence: 95%

“…To analyse the contamination and incompleteness of the constructed groups we followed Wojtak et al (2018), where the contamination (C) and incompleteness (I) are defined as…”

Section: Discussionmentioning

confidence: 99%

Bayesian group finder based on marked point processes

Tempel

Kruuse

Kipper

et al. 2018

A&A

Self Cite

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“…However, determining M * , tot in observations requires assigning cluster memberships to galaxies. This process can introduce its own, potentially hard to quantify, uncertainties and biases (see Wojtak et al 2018 for a discussion of how imperfect cluster membership affects mass estimates). Stellar mass based estimators therefore need to make a trade-off between reducing the intrinsic scatter of the observable (by including satellite masses) and adding scatter and bias in the cluster finder.…”

Section: Cen+n Stellar Massmentioning

confidence: 99%

“…A summary of both optical and other halo mass estimators is shown in Table 1. For a detailed review of the performance of optical estimators and how they are impacted by projection, we refer readers to Pearson et al (2015) and Wojtak et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Physical correlations of the scatter between galaxy mass, stellar content, and halo mass

Bradshaw

Leauthaud

Hearin

et al. 2020

Monthly Notices of the Royal Astronomical Society

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We use the UniverseMachine to analyze the source of scatter between the central galaxy mass, the total stellar mass in the halo, and the dark matter halo mass. We also propose a new halo mass estimator, the cen+N mass: the sum of the stellar mass of the central and the N most massive satellites. We show that, when real space positions are perfectly known, the cen+N mass has scatter competitive with that of richness-based estimators. However, in redshift space, the cen+N mass suffers less from projection effects in the UniverseMachine model. The cen+N mass is therefore a viable low scatter halo mass estimator, and should be considered an important tool to constrain cosmology with upcoming spectroscopic data from DESI. We analyze the scatter in stellar mass at fixed halo mass and show that the total stellar mass in a halo is uncorrelated with secondary halo properties, but that the central stellar mass is a function of both halo mass and halo age. This is because central galaxies in older halos have had more time to grow via accretion. If the UniverseMachine model is correct, accurate galaxy-halo modeling of mass selected samples therefore needs to consider halo age in addition to mass.

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“…37423corr (2002), Merchán & Zandivarez (2002), Eke et al (2004), Merchán & Zandivarez (2005), Berlind et al (2006), Tago et al (2006), Deng et al (2007), Tago et al (2008), Tago et al (2010), Calvi et al (2011), Muñoz-Cuartas & Müller (2012), Tempel et al (2012), Tempel et al (2014), Tempel et al (2017), among others. As this method is probably the most commonly used to identify groups of galaxies, several authors have focused on the analysis of its performance, such as for example Frederic (1994) and, more recently, Nurmi et al (2013), Duarte & Mamon (2014), Old et al (2014), Old et al (2015) , Old et al (2018), Wojtak et al (2018), Davies et al (2019). Beyond the demonstrated virtues of this algorithm and its flexibility to adapt to different scientific objectives (e.g., transverse and perpendicular linking lengths, scale factor), one of the negative aspects that can be extracted from these analyses is that the poor groups (that have 4 galaxies or less) obtained with this algorithm have low reliability and those which are numerous (more than 10 galaxies) are likely to have many interlopers.…”

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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Galaxy Cluster Mass Reconstruction Project – IV. Understanding the effects of imperfect membership on cluster mass estimation

Cited by 40 publications

References 60 publications

Bayesian group finder based on marked point processes

Bayesian group finder based on marked point processes

Physical correlations of the scatter between galaxy mass, stellar content, and halo mass

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

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