Group-finding Algorithm Research Articles

The evolution of star clusters: the resolved-star approach

We present the first results of a new technique to detect, locate, and characterize young dissolving star clusters. Using Hubble Space Telescope/Advanced Camera for Surveys archival images of the nearby galaxy IC2574, we performed stellar PSF photometry and selected the most massive stars as our first test sample. We used a group-finding algorithm on the selected massive stars to find cluster candidates. We then plot the color-magnitude diagrams for each group, and use stellar evolutionary models to estimate their age. So far, we found 79 groups with ages of up to about 100 Myr, displaying various sizes and densities.

A GROUP FINDING ALGORITHM FOR MULTIDIMENSIONAL DATA SETS

We describe a density-based hierarchical group finding algorithm capable of identifying structures and substructures of any shape and density in multidimensional data sets where each dimension can be a numeric attribute with arbitrary measurement scale. This has applications in a wide variety of fields from finding structures in galaxy redshift surveys, to identifying halos and subhalos in N-body simulations and group finding in Local Group chemodynamical data sets. In general, clustering schemes require an a priori definition of a metric (a non-negative function that gives the distance between two points in a space) and the quality of clustering depends upon this choice. The general practice is to use a constant global metric which is optimal only if the clusters in the data are self-similar. For complex data configurations even the most finely tuned constant global metric turns out to be suboptimal. Moreover, the correct choice of metric also becomes increasingly important as the number of dimensions increase. To address these problems, we present an entropy-based binary space partitioning algorithm which uses a locally adaptive metric for each data point. The metric is employed to calculate the density at each point and a list of its nearest neighbors, and this information is then used to form a hierarchy of groups. Finally, the ratio of maximum to minimum density of points in a group is used to estimate the significance of the groups. Setting a threshold on this significance can effectively screen out groups arising due to Poisson noise and helps organize the groups into meaningful clusters. For a data set of N points, the algorithm requires only O(N) space and O(N(log N)3) time which makes it ideally suitable for analyzing large data sets. As an example, we apply the algorithm to identify structures in a simulated stellar halo using the full six-dimensional phase space coordinates.

The Clustering of SDSS Galaxy Groups: Mass and Color Dependence

We use a sample of galaxy groups selected from the SDSS DR 4 with an adaptive halo-based group finder to probe how the clustering strength of groups depends on their masses and colors. In particular, we determine the relative biases of groups of different masses, as well as that of groups with the same mass but with different colors. In agreement with previous studies, we find that more massive groups are more strongly clustered, and the inferred mass dependence of the halo bias is in good agreement with predictions for the $\Lambda$CDM cosmology. Regarding the color dependence, we find that groups with red centrals are more strongly clustered than groups of the same mass but with blue centrals. Similar results are obtained when the color of a group is defined to be the total color of its member galaxies. The color dependence is more prominent in less massive groups and becomes insignificant in groups with masses $\gta 10^{14}\msunh$. We construct a mock galaxy redshift survey constructed from the large Millenium simulation that is populated with galaxies according to the semi-analytical model of Croton et al. Applying our group finder to this mock survey, and analyzing the mock data in exactly the same way as the true data, we are able to accurately recover the intrinsic mass and color dependencies of the halo bias in the model. This suggests that our group finding algorithm and our method of assigning group masses do not induce spurious mass and/or color dependencies in the group-galaxy correlation function. The semi-analytical model reveals the same color dependence of the halo bias as we find in our group catalogue. In halos with $M\sim 10^{12}\msunh$, though, the strength of the color dependence is much stronger in the model than in the data.

The assembly bias of dark matter haloes to higher orders

We use an extremely large volume (2.4h 3 Gpc 3 ), high resolution N-body simulation to measure the higher order clustering of dark matter haloes as a function of mass and internal structure. As a result of the large simulation volume and the use of a novel “cross-moment” counts-in-cells technique which suppresses discreteness noise, we are able to measure the clustering of haloes corresponding to rarer peaks than was possible in previous studies; the rarest haloes for which we measure the variance are 100 times more clustered than the dark matter. We are able to extract, for the first time, halo bias parameters from linear up to fourth order. For all orders measured, we find that the bias parameters are a strong function of mass for haloes more massive than the characteristic mass M�. Currently, no theoretical model is able to reproduce this mass dependence closely. We find that the bias parameters also depend on the internal structure of the halo up to fourth order. For haloes more massive than M�, we find that the more concentrated haloes are more weakly clustered than the less concentrated ones. We see no dependence of clustering on concentration for haloes with masses M < M�; this is contrary to the trend reported in the literature when segregating haloes by their formation time. Our results are insensitive to whether haloes are labelled by the total mass returned by the friends-of-friends group finder or by the mass of the most massive substructure. This implies that our conclusions are not an artefact of the particular choice of group finding algorithm. Our results will provide important input to theoretical models of galaxy clustering.

FINDING GALAXY GROUPS IN PHOTOMETRIC-REDSHIFT SPACE: THE PROBABILITY FRIENDS-OF-FRIENDS ALGORITHM

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 FoF algorithm in the transverse direction and the photometric-redshift probability densities in the radial dimension. The innovative characteristic of our group-finding algorithm is the improvement of redshift estimation via the constraints given by the transversely connected galaxies in a group, based on the assumption that all galaxies in a group have the same redshift. Tests using the Virgo Consortium Millennium Simulation mock catalogs allow us to show that the recovery rate of the pFoF algorithm is larger than 80% for mock groups of at least 2 × 1013 M☉, while the false detection rate is about 10% for pFoF groups containing around at least eight net members. Applying the algorithm to the Canadian Network for Observational Cosmology Survey group catalogs gives results which are consistent with the mock catalog tests. From all these results, we conclude that our group-finding algorithm offers an effective yet simple way to identify galaxy groups in photometric-redshift catalogs.

Are the nearby groups of galaxies gravitationally bound objects?

We have compared numerical simulations to observations for the nearby (< 40 Mpc) groups of galaxies (Huchra & Geller 1982 and Ramella et al. 2002). The group identi- fication is carried out using a group-finding algorithm developed by Huchra & Geller (1982). Using cosmological N-body simulation code with theCDM cosmology, we show that the dynamical properties of groups of galaxies identified from the simulation data are, in general, in a moderate, within 2σ, agreement with the observational cat- alogues of groups of galaxies. As simulations offer more dynamical information than observations, we used the N-body simulation data to calculate whether the nearby groups of galaxies are gravitationally bound objects by using their virial ratio. We show that in aCDM cosmology about 20 per cent of nearby groups of galaxies, identified by the same algorithm as in the case of observations, are not bound, but merely groups in a visual sense. This is quite significant, specifically because estima- tions of group masses in observations are often based on an assumption that groups of galaxies found by the friends-of-friends algorithm are gravitationally bound objects. Simulations with different resolutions show the same results. We also show how the fraction of gravitationally unbound groups varies when the apparent magnitude limit of the sample and the value of the cosmological constantis changed. In general, a larger value of thegenerates slightly more unbound groups.

Percolation Galaxy Groups and Clusters in the SDSS Redshift Survey: Identification, Catalogs, and the Multiplicity Function

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 same dark matter halos, using a set of mock catalogs created by populating halos of N-body simulations with galaxies. Extensive tests with these mock catalogs show that no combination of perpendicular and line-of-sight linking lengths is able to yield groups and clusters that simultaneously recover the true halo multiplicity function, projected size distribution, and velocity dispersion. We adopt a linking length combination that yields, for galaxy groups with ten or more members: a group multiplicity function that is unbiased with respect to the true halo multiplicity function; an unbiased median relation between the multiplicities of groups and their associated halos; a spurious group fraction of less than ~1%; a halo completeness of more than ~97%; the correct projected size distribution as a function of multiplicity; and a velocity dispersion distribution that is ~20% too low at all multiplicities. These results hold over a range of mock catalogs that use different input recipes of populating halos with galaxies. We apply our group-finding algorithm to the SDSS data and obtain three group and cluster catalogs for three volume-limited samples that cover 3495.1 square degrees on the sky. We correct for incompleteness caused by fiber collisions and survey edges, and obtain measurements of the group multiplicity function, with errors calculated from realistic mock catalogs. These multiplicity function measurements provide a key constraint on the relation between galaxy populations and dark matter halos.

A New Halo‐finding Method forN‐Body Simulations

We have developed a new halo-finding method called the physically self-bound (PSB) group-finding algorithm, which is designed to identify halos located in crowded regions. To demarcate subhalo regions, we introduce the tidal constraint combined with the self-boundedness check. In order to reduce overload and parallel finding, we first divide whole simulation particles into many local particle groups using two distinct methods. One adopts a density mesh used to group particles in connected local overdense cells, and the other applies the friends-of-friends (FoF) algorithm with a linking length (lloc) 0.3 times the mean particle separation. Then we divide each local particle group into several subgroups enclosed by numerous density levels and identify tidally stable and self-bound subhalos around density peaks. Subhalo finding is done on a fine mesh with cell size equal to twice the force resolution. Particles in a density shell that surrounds only one density peak form a subhalo candidate. Particles located in the outer remaining density shells that surround more than one peak become member candidates of one of the subhalos. We determine the membership using the tidal boundary constraint. We have found that the mass function is very insensitive to lloc when lloc ≥ 0.3. At least 40% of subhalos do not seem to collapse to the most massive subhalo in a FoF group when the Press & Schechter collapse model is applied to measure the size of a collapsed structure in the Lagrangian space. We have applied our halo-finding method to a 10243 particle simulation in a ΛCDM model and compare the halo mass functions with those previously found in the literature.

The evolution of substructure in galaxy, group and cluster haloes — III. Comparison with simulations

In a previous paper, we described a new method for including detailed information about substructure in semi-analytic models of halo formation based on merger trees. In this paper, we compare the predictions of our model with results from self-consistent numerical simulations. We find that in general the two methods agree extremely well, particularly once numerical effects and selection effects in the choice of haloes are taken into account. As expected from the original analyses of the simulations, we see some evidence for artificial overmerging in the innermost regions of the simulated haloes, either because substructure is being disrupted artificially or because the group-finding algorithms used to identify substructure are not detecting all the bound clumps in the highest-density regions. Our analytic results suggest that greater mass and force resolution may be required before numerical overmerging becomes negligible in all current applications. We discuss the implications of this result for observational and experimental tests of halo substructure, such as the analysis of discrepant magnification ratios in strongly lensed systems, terrestrial experiments to detect dark matter particles directly or indirect detection experiments searching for positrons, gamma-rays, neutrinos or other dark matter decay or annihilation products.

The environmental dependence of radio-loud AGN activity and star formation in the 2dFGRS

By combining the 2-degree Field Galaxy Redshift Survey with the NRAO VLA Sky Survey at 1.4 GHz, the environments of radio-loud active galactic nuclei (AGN) in the nearby Universe are investigated using both local projected galaxy densities and a friends-of-friends group-finding algorithm. Radio-loud AGN are preferentially located in galaxy groups and poor-to-moderate richness galaxy clusters. The AGN fraction appears to depend more strongly on the large-scale environment (group, cluster, etc.) in which a galaxy is located than on its more local environment, except at the lowest galaxy surface densities where practically no radio-loud AGN are found. The ratio of absorption-line to emission-line AGN changes dramatically with environment, with essentially all radio-loud AGN in rich environments showing no emission lines. This result could be connected with the lack of cool gas in cluster galaxies, and may have important consequences for analyses of optically selected AGN, which are invariably selected on emission-line properties. The local galaxy surface density of the absorption-line AGN is strongly correlated with radio luminosity, implying that the radio luminosities may be significantly boosted in dense environments due to confinement by the hot intracluster gas. The environments of a radio-selected sample of star-forming galaxies are also investigated to provide an independent test of optical studies. In line with those studies, the fraction of star-forming galaxies is found to decrease strongly with increasing local galaxy surface density; this correlation extends across the whole range of galaxy surface densities, with no evidence for the density threshold found in some optical studies.

Galaxy groups in the 2dFGRS: the group-finding algorithm and the 2PIGG catalogue

The construction of a catalogue of galaxy groups from the Two-degree Field Galaxy Redshift Survey (2dFGRS) is described. Groups are identified by means of a friends-of-friends percolation algorithm which has been thoroughly tested on mock versions of the 2dFGRS generated from cosmological N-body simulations. The tests suggest that the algorithm groups all galaxies that it should be grouping, with an additional 40 per cent of interlopers. About 55 per cent of the ∼190 000 galaxies considered are placed into groups containing at least two members of which ∼29 000 are found. Of these, ∼7000 contain at least four galaxies, and these groups have a median redshift of 0.11 and a median velocity dispersion of 260 km s−1. This 2dFGRS Percolation-Inferred Galaxy Group (2PIGG) catalogue represents the largest available homogeneous sample of galaxy groups. It is publicly available on the World Wide Web.

Galaxy groups in the 2dF Galaxy Redshift Survey: the catalogue

We construct a galaxy groups catalogue from the public 100-K data release of the 2dF Galaxy Redshift Survey. The group identification is carried out using a slightly modified version of the group-finding algorithm developed by Huchra & Geller. Several tests using mock catalogues allow us to find the optimal conditions to increase the reliability of the final group sample. A minimum number of four members, an outer number density enhancement of 80 and a linking radial cut-off of 200 km s' are the best obtained values from the analysis. Using these parameters, approximately 90 per cent of groups identified in real space have a redshift space counterpart. On the other hand, the level of contamination in redshift space reaches 30 per cent, including ∼6 per cent of artificial groups and ∼24 per cent of groups associated with binaries or triplets in real space. The final sample comprises 2209 galaxy groups covering the sky region described by Colless et al. spanning over the redshift range of 0.003 ≤ z ≤ 0.25 with a mean redshift of 0.1.

The Optical Luminosity Function of Virialized Systems

We determine the optical luminosity function of virialized systems over the full range of density enhancements, from single galaxies to clusters of galaxies. The analysis is based on galaxy systems identified from the Nearby Optical Galaxy (NOG) sample, which is the largest all-sky catalog of objectively identified bound objects presently available. We find that the B-band luminosity function of systems is insensitive to the choice of group-finding algorithms and is well described, over the absolute magnitude range -24.5 ≤ M - 5 log h75 ≤ -18.5, by a Schechter function with αs = -1.4 ± 0.03, M - 5 log h75 = -23.1 ± 0.06, and = 4.8 × 10-4 h Mpc-3, or by a double power law: pl(Ls) ∝ L for Ls Lpl, with Lpl = 8.5 × 1010 h L☉, corresponding to Ms - 5 log h75 = -21.85. The characteristic luminosity of virialized systems, Lpl, is ~3 times that (L) of the NOG galaxies. Our results show that half of the luminosity of the universe is generated in systems with Ls 30L. We find a significant environmental dependence in the luminosity function of systems, in the sense that overdense regions, as measured on scales of 5 h-1 Mpc, preferentially host brighter, and presumably more massive, virialized systems.

The Ω0Dependence of the Evolution of ξ(r)

The evolution of the two-point correlation function, ξ(r, z), and the pairwise velocity dispersion, σ(r, z), for both the matter, ξρρ, and halo population, ξhh, in three different cosmological models, (Ω0, λ0) = (1, 0), (0.2, 0), and (0.2, 0.8), are described. If the evolution of ξ is parameterized by ξ(r, z) = (1 + z)-(3+)ξ(r, 0), where ξ(r, 0) = (r/r0)-γ, then ρρ ranges from 1.04 ± 0.09 for (1, 0) and 0.18 ± 0.12 for (0.2, 0), as measured by the evolution of ξρρ at 1 Mpc (from z ~ 5 to the present epoch). For halos, depends indeed on their mean overdensity. Halos with a mean overdensity of ~2000 were used to compute the halo two-point correlation function, ξhh, tested with two different group-finding algorithms: the friends of friends algorithm and the spherical overdensity algorithm. It is certainly believed that the rate of growth of this ξhh will give a good estimate of the evolution of the galaxy two-point correlation function, at least from z ~ 1 to the present epoch. The values we get for hh range from 1.54 for (1, 0) to -0.36 for (0.2, 0), as measured by the evolution of ξhh from z ~ 1.0 to the present epoch. These values could be used to constrain the cosmological scenario.

Cluster correlation functions in N-body simulations

The correlation function of galaxy clusters has often been used as a test of cosmological models. A number of assumptions are implicit in the comparison of theoretical expectations to data. Here we use an ensemble of ten large N-body simulations of the standard cold dark matter cosmology to investigate how cluster selection criteria and other uncertain factors influence the cluster correlation function. We consider the effects of varying the definition of a cluster, the mean number density (or equivalently the richness) in a catalogue, and the assumed normalisation of the model; we also examine the importance of redshift space distortions. We implement five different group-finding algorithms and construct cluster catalogues defined by mass, velocity dispersion or a measure of X-ray luminosity. We find that different cluster catalogues yield correlation functions which can differ by substantially more than the statistical errors in any one determination. For a given cluster selection criteria, the correlation length typically varies by $\sim 20\%$ in catalogues spanning the range of intercluster separations covered by the APM and Abell (richness class $\gsim 1$) catalogues. Distortions produced by peculiar velocities in redshift space enhance the correlation function at large separations and lead to a larger clustering length in redshift space than in real space. The sensitivity of the cluster correlation function to various uncertain model assumptions substantially weakens previous conclusions based on the comparison of model predictions with real data. Detailed modelling of cluster selection procedures including the effects of selecting from projected galaxy catalogues is required before the cluster correlation function can be regarded as a high precision constraint on cosmological models.

The Existence of Moving Groups and the Disk Heating Problem

A powerful moving group-finding algorithm has been developed and applied to a large sample of B, A and F main sequence stars from the Hipparcos Input Catalogue. Four moving groups near the Sun (Pleiades, Sirius, IC2391 and Hyades) have been identified without assuming any a priori knowledge of the properties of moving groups. This is the most convincing objective evidence for their existence. After the members of the moving groups are removed from the sample, we investigate the disk heating problem. The results show that the velocity dispersion of the local disk stars increases with age, roughly as ≃ ∞ τ1/5.

The stellar populations of NGC 6822

view Abstract Citations (42) References (44) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS The Stellar Populations of NGC 6822 Wilson, Christine D. Abstract New CCD BV photometry is presented for blue and red stars in a large region of the Local Group dwarf irregular galaxy NGC 6822. The total reddening (foreground+internal) estimated from the median color of the blue plume is E(B- V) = 0.45. The slope of the luminosity function for blue stars with 17 < V <20 is 0.57+/- 0.10, in good agreement with previous studies for this and other galaxies. The OB associations are reidentified using an objective, automated group-finding algorithm. The properties of the OB associations in NGC 6822, such as mass and diameter, agree well with OB association properties in other galaxies. The red-to- blue supergiant ratio is found to be similar in NGC 6822, M33, and IC 1613, despite a range in metallicity of more than a factor often, which suggests that the red-to-blue ratio is not a sensitive function of metallicity. Finally, an attempt to determine the relative numbers of Wolf-Rayet stars and O start in NGC 6822 shows that the ratio N_WR_/N_OB_ can be constrained only to within a factor of 20, unless spectroscopic observations are available for a large sample of O stars. Publication: The Astronomical Journal Pub Date: October 1992 DOI: 10.1086/116327 Bibcode: 1992AJ....104.1374W Keywords: Blue Stars; Dwarf Galaxies; Irregular Galaxies; Local Group (Astronomy); O Stars; Stellar Spectrophotometry; Color-Magnitude Diagram; Data Reduction; Point Spread Functions; Stellar Luminosity; Supergiant Stars; Wolf-Rayet Stars; Astrophysics; GALAXIES: INDIVIUAL: NGC 6822; GALAXIES: STELLAR CONTENT full text sources ADS | data products SIMBAD (427) NED (1) Related Materials (1) Erratum: 1995AJ....109..449W