Related papers: A new approach to finding galaxy groups using Mark…
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.…
We present a structure finding algorithm designed to identify galaxy groups in photometric redshift data sets: the probability friends-of-friends (pFoF) algorithm. This algorithm is derived by combining the friends-of-friends algorithm in…
In this work we introduce a new method to perform the identification of groups of galaxies and present results of the identification of galaxy groups in the Seventh Data Release of the Sloan Digital Sky Survey (SDSS-DR7). Our methodology…
A direct approach to studying the galaxy-halo connection is to analyze groups and clusters of galaxies that trace the underlying dark matter halos, emphasizing the importance of identifying galaxy clusters and their associated brightest…
We present a modified version of the friends-of-friends (FOF) structure finding algorithm, designed specifically to locate groups or clusters of galaxies in photometric redshift datasets. The main objective of this paper is to show that…
Galaxy groups and clusters are the main tools used to test cosmological models and to study the environmental effect of galaxy formation. This work provides a catalogue of galaxy groups and clusters, as well as potentially merging systems…
We present a catalogue of galaxy groups and clusters selected using a friends-of-friends algorithm with a dynamic linking length from the 2dF-SDSS and QSO (2SLAQ) luminous red galaxy survey. The linking parameters for the code are chosen…
The majority of galaxy group catalogues available in the literature use the popular friends-of-friends algorithm which links galaxies using a linking length. One potential drawback to this approach is that clusters of point can be link with…
We identify galaxy groups and clusters in volume-limited samples of the SDSS redshift survey, using a redshift-space friends-of-friends algorithm. We optimize the friends-of-friends linking lengths to recover galaxy systems that occupy the…
We create a new catalogue of groups and clusters for the 2dF GRS final release sample. We show that the variable linking length friends-of-friends (FoF) algorithms used so far yield groups with sizes that grow systematically with distance…
Identifying galaxy groups from redshift surveys of galaxies plays an important role in connecting galaxies with the underlying dark matter distribution. Current and future high-$z$ spectroscopic surveys, usually incomplete in redshift…
We have performed a detailed analysis of the ability of the friends-of-friends algorithm in identifying real galaxy systems in deep surveys such as the future Javalambre Physics of the Accelerating Universe Astrophysical Survey. Our…
We describe an extension of the halo-based galaxy group-finding algorithm. We add freedom to the algorithm in order to more accurately determine which galaxies are central and which are satellites, and to provide unbiased estimates of halo…
We test the Friends-of-Friends (FoF) grouping algorithm, which depends on two linking lengths (LLs), plane-of-sky and line-of-sight (LOS), normalized to the mean nearest neighbor separation of field galaxies. We argue, on theoretical…
Combining our knowledge of halo structure and internal kinematics from cosmological dark matter simulations and the distribution of halo interlopers in projected phase space measured in cosmological galaxy simulations, we develop MAGGIE, a…
The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the…
Context. Groups form the most abundant class of galaxy systems. They act as the principal drivers of galaxy evolution and can be used as tracers of the large-scale structure and the underlying cosmology. However, the detection of galaxy…
The development of algorithms for unsupervised pattern recognition by nonlinear clustering is a notable problem in data science. Markov clustering (MCL) is a renowned algorithm that simulates stochastic flows on a network of sample…
We identify compact groups of galaxies (CGs) in the IllustrisTNG-300 simulation using a Friends-of-Friends (FoF) algorithm. Our approach is designed to be comparable to systematic CG searches based on spectroscopic surveys, while avoiding…
Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…