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Register a new userMultimodality in galaxy clusters from SDSS DR8: substructure and velocity distribution
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We search for the presence of substructure, a non-Gaussian, asymmetrical velocity distribution of galaxies, and large peculiar velocities of the main galaxies in galaxy clusters with at least 50 member galaxies, drawn from the SDSS DR8. We employ a number of 3D, 2D, and 1D tests to analyse the distribution of galaxies in clusters: 3D normal mixture modelling, the Dressler-Shectman test, the Anderson-Darling and Shapiro-Wilk tests and others. We find the peculiar velocities of the main galaxies, and use principal component analysis to characterise our results. More than 80% of the clusters in our sample have substructure according to 3D normal mixture modelling, the Dressler-Shectman (DS) test shows substructure in about 70% of the clusters. The median value of the peculiar velocities of the main galaxies in clusters is 206 km/s (41% of the rms velocity). The velocities of galaxies in more than 20% of the clusters show significant non-Gaussianity. While multidimensional normal mixture modelling is more sensitive than the DS test in resolving substructure in the sky distribution of cluster galaxies, the DS test determines better substructure expressed as tails in the velocity distribution of galaxies. Richer, larger, and more luminous clusters have larger amount of substructure and larger (compared to the rms velocity) peculiar velocities of the main galaxies. Principal component analysis of both the substructure indicators and the physical parameters of clusters shows that galaxy clusters are complicated objects, the properties of which cannot be explained with a small number of parameters or delimited by one single test. The presence of substructure, the non-Gaussian velocity distributions, as well as the large peculiar velocities of the main galaxies, shows that most of the clusters in our sample are dynamically young.
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We study the relations between the multimodality of galaxy clusters drawn from the SDSS DR8 and the environment where they reside. As cluster environment we consider the global luminosity density field, supercluster membership, and supercluster morphology. We use 3D normal mixture modelling, the Dressler-Shectman test, and the peculiar velocity of cluster main galaxies as signatures of multimodality of clusters. We calculate the luminosity density field to study the environmental densities around clusters, and to find superclusters where clusters reside. We determine the morphology of superclusters with the Minkowski functionals and compare the properties of clusters in superclusters of different morphology. We apply principal component analysis to study the relations between the multimodality parametres of clusters and their environment simultaneously. We find that multimodal clusters reside in higher density environment than unimodal clusters. Clusters in superclusters have higher probability to have substructure than isolated clusters. The superclusters can be divided into two main morphological types, spiders and filaments. Clusters in superclusters of spider morphology have higher probabilities to have substructure and larger peculiar velocities of their main galaxies than clusters in superclusters of filament morphology. The most luminous clusters are located in the high-density cores of rich superclusters. Five of seven most luminous clusters, and five of seven most multimodal clusters reside in spider-type superclusters; four of seven most unimodal clusters reside in filament-type superclusters. Our study shows the importance of the role of superclusters as high density environment which affects the properties of galaxy systems in them.
We investigate the relationship between star formation (SF) and substructure in a sample of 107 nearby galaxy clusters using data from the Sloan Digital Sky Survey (SDSS). Several past studies of individual galaxy clusters have suggested that cluster mergers enhance cluster SF, while others find no such relationship. The SF fraction in multi-component clusters (0.228 +/- 0.007) is higher than that in single-component clusters (0.175 +/- 0.016) for galaxies with M^0.1_r < -20.5. In both single- and multi-component clusters, the fraction of star-forming galaxies increases with clustercentric distance and decreases with local galaxy number density, and multi-component clusters show a higher SF fraction than single-component clusters at almost all clustercentric distances and local densities. Comparing the SF fraction in individual clusters to several statistical measures of substructure, we find weak, but in most cases significant at greater than 2 sigma, correlations between substructure and SF fraction. These results could indicate that cluster mergers may cause weak but significant SF enhancement in clusters, or unrelaxed clusters exhibit slightly stronger SF due to their less evolved states relative to relaxed clusters.
We describe redMaPPer, a new red-sequence cluster finder specifically designed to make optimal use of ongoing and near-future large photometric surveys. The algorithm has multiple attractive features: (1) It can iteratively self-train the red-sequence model based on minimal spectroscopic training sample, an important feature for high redshift surveys; (2) It can handle complex masks with varying depth; (3) It produces cluster-appropriate random points to enable large-scale structure studies; (4) All clusters are assigned a full redshift probability distribution P(z); (5) Similarly, clusters can have multiple candidate central galaxies, each with corresponding centering probabilities; (6) The algorithm is parallel and numerically efficient: it can run a Dark Energy Survey-like catalog in ~500 CPU hours; (7) The algorithm exhibits excellent photometric redshift performance, the richness estimates are tightly correlated with external mass proxies, and the completeness and purity of the corresponding catalogs is superb. We apply the redMaPPer algorithm to ~10,000 deg^2 of SDSS DR8 data, and present the resulting catalog of ~25,000 clusters over the redshift range 0.08<z<0.55. The redMaPPer photometric redshifts are nearly Gaussian, with a scatter sigma_z ~ 0.006 at z~0.1, increasing to sigma_z~0.02 at z~0.5 due to increased photometric noise near the survey limit. The median value for |Delta z|/(1+z) for the full sample is 0.006. The incidence of projection effects is low (<=5%). Detailed performance comparisons of the redMaPPer DR8 cluster catalog to X-ray and SZ catalogs are presented in a companion paper (Rozo & Rykoff 2014).
Detecting the large-scale structure of the Universe based on the galaxy distribution and characterising its components is of fundamental importance in astrophysics but is also a difficult task to achieve. Wide-area spectroscopic redshift surveys are required to accurately measure galaxy positions in space that also need to cover large areas of the sky. It is also difficult to create algorithms that can extract cosmic web structures (e.g. filaments). Moreover, these detections will be affected by systematic uncertainties that stem from the characteristics of the survey used (e.g. its completeness and coverage) and from the unique properties of the specific method adopted to detect the cosmic web (i.e. the assumptions it relies on and the free parameters it may employ). For these reasons, the creation of new catalogues of cosmic web features on wide sky areas is important, as this allows users to have at their disposal a well-understood sample of structures whose systematic uncertainties have been thoroughly investigated. In this paper we present the filament catalogues created using the discrete persistent structure extractor (DisPerSE) tool in the Sloan Digital Sky Survey (SDSS), and we fully characterise them in terms of their dependence on the choice of parameters pertaining to the algorithm, and with respect to several systematic issues that may arise in the skeleton as a result of the properties of the galaxy distribution (such as Finger-of-God redshift distortions and defects of the density field that are due to the boundaries of the survey).
We present a new approach for quantifying the abundance of galaxy clusters and constraining cosmological parameters using dynamical measurements. In the standard method, galaxy line-of-sight (LOS) velocities, $v$, or velocity dispersions are used to infer cluster masses, $M$, in order to quantify the halo mass function (HMF), $dn(M)/dlog(M)$, which is strongly affected by mass measurement errors. In our new method, the probability distribution of velocities for each cluster in the sample are summed to create a new statistic called the velocity distribution function (VDF), $dn(v)/dv$. The VDF can be measured more directly and precisely than the HMF and it can also be robustly predicted with cosmological simulations which capture the dynamics of subhalos or galaxies. We apply these two methods to mock cluster catalogs and forecast the bias and constraints on the matter density parameter $Omega_m$ and the amplitude of matter fluctuations $sigma_8$ in flat $Lambda$CDM cosmologies. For an example observation of 200 massive clusters, the VDF with (without) velocity errors constrains the parameter combination $sigma_8Omega_m^{0.29 (0.29)} = 0.587 pm 0.011 (0.583 pm 0.011)$ and shows only minor bias. However, the HMF with dynamical mass errors is biased to low $Omega_m$ and high $sigma_8$ and the fiducial model lies well outside of the forecast constraints, prior to accounting for Eddington bias. When the VDF is combined with constraints from the cosmic microwave background (CMB), the degeneracy between cosmological parameters can be significantly reduced. Upcoming spectroscopic surveys that probe larger volumes and fainter magnitudes will provide a larger number of clusters for applying the VDF as a cosmological probe.
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