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Register a new userCharacterising superclusters with the galaxy cluster distribution
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Superclusters are the largest, observed matter density structures in the Universe. Recently Chon et al.(2013) presented the first supercluster catalogue constructed with a well-defined selection function based on the X-ray flux-limited cluster survey, REFLEX II. For the construction of the sample we proposed a concept to find the large objects with a minimum overdensity such that most of their mass will collapse in the future. The main goal of the paper is to provide support for our concept using simulations that we can, on the basis of our observational sample of X-ray clusters, construct a supercluster sample defined by a certain minimum overdensity, and to test how superclusters trace the underlying dark matter distribution. Our results confirm that an overdensity in the number of clusters is tightly correlated with an overdensity of the dark matter distribution. This enables us to define superclusters such that most of the mass will collapse in the future and to get first-order mass estimates of superclusters on the basis of the properties of the member clusters. We also show that in this context the ratio of the cluster number density and dark matter mass density is consistent with the theoretically expected cluster bias. Our previous work provided evidence that superclusters are a special environment for density structures of the dark matter to grow differently from the field as characterised by the X-ray luminosity function. Here we confirm for the first time that this originates from a top-heavy mass function at high statistical significance provided by a Kolmogorov-Smirnov test. We also find in close agreement with observations that the superclusters occupy only a small volume of few percent while they contain more than half of the clusters in the present day Universe.
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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).
Groups of galaxies are the most common cosmic structures. However, due to the poor statistics, projection effects and the lack of accurate distances, our understanding of their dynamical and evolutionary status is still limited. This is particularly true for the so called Shakhbazyan groups (SHK) which are still largely unexplored due to the lack of systematic spectroscopic studies of both their member galaxies and the surrounding environment. In our previous paper, we investigated the statistical properties of a large sample of SHK groups using Sloan Digital Sky Survey data and photometric redshifts. Here we present the follow-up of 5 SHK groups (SHK 10, 71, 75, 80, 259) observed within our spectroscopic campaign with the Telescopio Nazionale Galileo, aimed at confirming their physical reality and strengthening our photometric results. For each of the selected groups we were able to identify between 6 and 13 spectroscopic members, thus confirming the robustness of the photometric redshift approach in identifying real galaxy over-densities. Consistently with the finding of our previous paper, the structures studied here have properties spanning from those of compact and isolated groups to those of loose groups. For what the global physical properties are concerned (total mass, mass-to-light ratios, etc.), we find systematic differences with those reported in the literature by previous studies. Our analysis suggests that previous results should be revisited; we show in fact that, if the literature data are re-analysed in a consistent and homogeneous way, the properties obtained are in agreement with those estimated for our sample.
In this work, we studied the impact of galaxy morphology on photometric redshift (photo-$z$) probability density functions (PDFs). By including galaxy morphological parameters like the radius, axis-ratio, surface brightness and the Sersic index in addition to the $ugriz$ broadbands as input parameters, we used the machine learning photo-$z$ algorithm ANNz2 to train and test on galaxies from the Canada-France-Hawaii Telescope Stripe-82 (CS82) Survey. Metrics like the continuous ranked probability score (CRPS), probability integral transform (PIT), Bayesian odds parameter, and even the width and height of the PDFs were evaluated, and the results were compared when different number of input parameters were used during the training process. We find improvements in the CRPS and width of the PDFs when galaxy morphology has been added to the training, and the improvement is larger especially when the number of broadband magnitudes are lacking.
Using a new compilation of available data on galaxy clusters and superclusters we present evidence for a quasiregular three-dimensional network of rich superclusters and voids, with the regions of high density separated by about 120 Mpc. We calculate the power spectrum for clusters of galaxies; it has a peak on the wavelength equal to the step of the network; the excess in the amplitude of the spectrum over that of the cold dark matter model is by a factor of 1.4. The probability that the spectrum can be formed within the framework of the standard cosmogony is very small. If the cluster distribution reflects the distribution of all matter (luminous and dark), then there must exists some hithero unknown process that produces regular structure on large scales.
In this paper we investigate the strong lensing statistics in galaxy clusters. We extract dark matter haloes from the Millennium-XXL simulation, compute their Einstein radius distribution, and find a very good agreement with Monte Carlo predictions produced with the MOKA code. The distribution of the Einstein radii is well described by a log-normal distribution, with a considerable fraction of the largest systems boosted by different projection effects. We discuss the importance of substructures and triaxiality in shaping the size of the critical lines for cluster size haloes. We then model and interpret the different deviations, accounting for the presence of a Bright Central Galaxy (BCG) and two different stellar mass density profiles. We present scaling relations between weak lensing quantities and the size of the Einstein radii. Finally we discuss how sensible is the distribution of the Einstein radii on the cosmological parameters {Omega}_M-{sigma}_8 finding that cosmologies with higher {Omega}_M and {sigma}_8 possess a large sample of strong lensing clusters. The Einstein radius distribution may help distinguish Planck13 and WMAP7 cosmology at 3{sigma}.
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