Bayesian group finder based on marked point processes

Abstract

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.

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

“…We introduce a simple algorithm, called FG, that identifies locations of clusters by looking for the Fingerof-God effect (FOG). Similar algorithms were introduced in the literature to identity FOG (e.g., Yoon et al 2008;Wen et al 2009;Tempel et al 2018). In this paper, we aim to construct a sample of galaxy clusters using the FG identification in the optical band using a highquality spectroscopic dataset.…”

The 2MASS redshift survey galaxy group catalogue derived from a graph-theory based friends-of-friends algorithm

“…There have been several proposed methods of determining the projected group center amongst different surveys and catalogues (Crook et al 2007;Poggianti et al 2010;Robotham et al 2011;Tempel et al 2018). Several techniques were explored using the TAO mock catalogue, such as: luminosity-weighted and flux-weighted moments, and the simple geometric center.…”

“…They form part of larger structures such as groups, clusters, filaments and voids that together compose the so-called cosmic web (Davis et al 1982;Zeldovich et al 1982;Klypin & Shandarin 1993;Bond et al 1996;Jarrett 2004). These large-scale structures are immediately revealed when visually examining the distributions of galaxy positions in redshift space (Peebles 1980;Huchra & Geller 1982;Tully & Fisher 1987;Peebles 1993;Fairall 1998;Huchra et al 2005;Crook et al 2007;Robotham et al 2011;Huchra et al 2012;Tempel et al 2016Tempel et al , 2018. However, visually identifying and classifying structures is a subjective method that is neither robust nor quantitative (Crook et al 2007;Robotham et al 2011;Huchra et al 2012;Alpaslan et al 2014;Saulder et al 2016;Jarrett et al 2017;Kourkchi & Tully 2017).…”

“…Redshift surveys provide one of the most convenient and efficient tools to study the three-dimensional distribution of galaxies. Being able to objectively identify large-scale structures within these surveys is relevant to addressing a number of unanswered questions in observational cosmology related to the CMB dipole anisotropy (Rauzy & Gurzadyan 1998;Bilicki et al 2011), establishing a relationship between galaxies and dark matter halos and probing dark matter interactions (Coutinho 2016;Lu et al 2016), constraining cosmological models (Peebles 1980), and exploring the effect of environment on galaxy evolution (Dressler 1980;Tempel et al 2016Tempel et al , 2018. Redshift surveys come in a variety of flavors; the two most common ones are those constrained to a small solid angle on the sky and targeting galaxies at high redshifts (so-called "pencil beam" surveys), and those covering larger fractions of the sky at the expense of depth.…”

A simulated annealing algorithm to quantify patterns in astronomical data

“…Storvik 1994; E-mail: mchira@physics.auth.gr 2005), physics and astronomy (c.f. Habib et al 2006;Stoica et al 2008;Tempel et al 2014b;Kovalenko et al 2017;Tempel et al 2018), etc. In such applications the optimum solution corresponds to that of the minimum energy state yearned during the annealing of solids, while the cost function, corresponding to the quantity to be minimized (e.g., cost of a construction, weight or volume of structures, time required for completing a task or any energy-like parameter), is analogous to the energy of the material.…”