Searching for the scale of homogeneity

We introduce a statistical quantity, known as the K function, related to the integral of the two-point correlation function. It gives us straightforward information about the scale where clustering dominates and the scale at which homogeneity is reached. We evaluate the correlation dimension, D ₂, as the local slope of the log–log plot of the K function. We apply this statistic to several stochastic point fields, to three numerical simulations describing the distribution of clusters and finally to real galaxy redshift surveys. Four different galaxy catalogues have been analysed using this technique: the Center for Astrophysics I, the Perseus–Pisces redshift surveys (these two lying in our local neighbourhood), the Stromlo–APM and the 1.2-Jy IRAS redshift surveys (these two encompassing a larger volume). In all cases, this cumulant quantity shows the fingerprint of the transition to homogeneity. The reliability of the estimates is clearly demonstrated by the results from controllable point sets, such as the segment Cox processes. In the cluster distribution models, as well as in the real galaxy catalogues, we never see long plateaus when plotting D ₂ as a function of the scale, leaving no hope for unbounded fractal distributions.     

The impact of dust on the scaling properties of galaxy clusters

We investigate the effect of dust on the scaling properties of galaxy clusters based on hydrodynamic N -body simulations of structure formation. We have simulated five dust models plus radiative cooling and adiabatic models using the same initial conditions for all runs. The numerical implementation of dust was based on the analytical computations of Montier & Giard. We set up dust simulations to cover different combinations of dust parameters that make evident the effects of size and abundance of dust grains. Comparing our radiative plus dust cooling runs with a purely radiative cooling simulation, we find that dust has an impact on cluster scaling relations. It mainly affects the normalization of the scalings (and their evolution), whereas it introduces no significant differences in their slopes. The strength of the effect critically depends on the dust abundance and grain size parameters as well as on the cluster scaling. Indeed, cooling due to dust is effective in the cluster regime and has a stronger effect on the 'baryon driven' statistical properties of clusters such as   L _X– M , Y – M , S – M   scaling relations. Major differences, relative to the radiative cooling model, are as high as 25 per cent for the   L _X– M   normalization, and about 10 per cent for the Y – M and S – M normalizations at redshift zero. On the other hand, we find that dust has almost no impact on the 'dark matter driven'   T _mw– M   scaling relation. The effects are found to be dependent in equal parts on both dust abundances and grain size distributions for the scalings investigated in this paper. Higher dust abundances and smaller grain sizes cause larger departures from the radiative cooling (i.e. with no dust) model.     

The 2dF Galaxy Redshift Survey: luminosity dependence of galaxy clustering

We investigate the dependence of the strength of galaxy clustering on intrinsic luminosity using the Anglo-Australian two degree field galaxy redshift survey (2dFGRS). The 2dFGRS is over an order of magnitude larger than previous redshift surveys used to address this issue. We measure the projected two-point correlation function of galaxies in a series of volume-limited samples. The projected correlation function is free from any distortion of the clustering pattern induced by peculiar motions and is well described by a power law in pair separation over the range     . The clustering of     galaxies in real space is well-fitted by a correlation length     and power-law slope     . The clustering amplitude increases slowly with absolute magnitude for galaxies fainter than M *, but rises more strongly at higher luminosities. At low luminosities, our results agree with measurements from the Southern Sky Redshift Survey 2 by Benoist et al. However, we find a weaker dependence of clustering strength on luminosity at the highest luminosities. The correlation function amplitude increases by a factor of 4.0 between     and −22.5, and the most luminous galaxies are 3.0 times more strongly clustered than L * galaxies. The power-law slope of the correlation function shows remarkably little variation for samples spanning a factor of 20 in luminosity. Our measurements are in very good agreement with the predictions of the hierarchical galaxy formation models of Benson et al.     

Galaxy cluster mergers

We present the results of an Eulerian adaptive mesh refinement (AMR) hydrodynamical and N -body simulation in a Λ cold dark matter (ΛCDM) cosmology. The simulation incorporates common cooling and heating processes for a primordial gas. A specific halo finder has been designed and applied in order to extract a sample of galaxy clusters directly obtained from the simulation without considering any resimulating scheme. We have studied the evolutionary history of the cluster haloes, and classified them into three categories depending on the merger events they have undergone: major mergers, minor mergers and relaxed clusters. The main properties of each one of these classes and the differences among them are discussed. The collisions among galaxy clusters are produced naturally by the non-linear evolution in the simulated cosmological volume; no controlled collisions have been considered. We pay special attention to discuss the role of merger events as a source of feedback and reheating, and their effects on the existence of cool cores in galaxy clusters, as well as in the scaling relations.     

The complex iron line in NGC 7469 observed by BeppoSAX

We compare the large-scale galaxy clustering in the new Sloan Digital Sky Survey (SDSS) early data release (EDR) with the clustering in the APM Galaxy Survey. We cut out pixel maps (identical in size and shape) from the SDSS and APM data to allow a direct comparison of the clustering. Here we concentrate our analysis on an equatorial SDSS strip in the South Galactic Cap (EDR/SGC) of 166 deg², 25 wide and 65° long . Only galaxies with Petrosian magnitudes  16.8< g '<19.8  are included to match the surface density of the  17< b _J<20  APM pixel maps (median depth of ∼400  h ⁻¹ Mpc). Both the amplitude and the shape of the angular two-point function and variance turn out to be in very good agreement with the APM on scales smaller than 2° (or ≲15  h ⁻¹ Mpc). The three-point function and skewness are also in excellent agreement within a 90 per cent confidence level. On the one hand these results illustrate that the EDR data and SDSS software pipelines work well and are suitable to carry out analysis of large-scale clustering. On the other hand they confirm that large-scale clustering analysis is becoming 'repeatable' and therefore that our conclusions for structure formation models seem to stand on solid scientific grounds.     

The real- and redshift-space density distribution functions for large-scale structure in the spherical collapse approximation

We use the spherical collapse (SC) approximation to derive expressions for the smoothed redshift-space probability distribution function (PDF), as well as the p -order hierarchical amplitudes S _p , in both real and redshift space. We compare our results with numerical simulations, focusing on the     standard CDM model, where redshift distortions are strongest. We find good agreement between the SC predictions and the numerical PDF in real space even for     , where σ _L is the linearly evolved rms fluctuation on the smoothing scale. In redshift space, reasonable agreement is possible only for     . Numerical simulations also yield a simple empirical relation between the real-space PDF and the redshift-space PDF: we find that for     , the redshift-space PDF, [ P δ _{( z )}], is, to a good approximation, a simple rescaling of the real-space PDF, P [ δ ], i.e.,     where σ and σ _{( z )} are the real-space and redshift-space rms fluctuations, respectively. This result applies well beyond the validity of linear perturbation theory, and it is a good fit for both the standard CDM model and the ΛCDM model. It breaks down for SCDM at     , but provides a good fit to the ΛCDM models for σ _L as large as 0.8.     

On the nature of bulges in general and of box/peanut bulges in particular: input from N-body simulations

Objects designated as bulges in disc galaxies do not form a homogeneous class. I distinguish three types: the classical bulges, the properties of which are similar to those of ellipticals and which form by collapse or merging; boxy and peanut bulges, which are seen in near-edge-on galaxies and which are in fact just a part of the bar seen edge-on; and, finally, disc-like bulges, which result from the inflow of (mainly) gas to the centre-most parts, and subsequent star formation. I make a detailed comparison of the properties of boxy and peanut bulges with those of N -body bars seen edge-on, and answer previously voiced objections about the links between the two. I also present and analyse simulations where a boxy/peanut feature is present at the same time as a classical spheroidal bulge, and compare them with observations. Finally, I propose a nomenclature that can help to distinguish between the three types of bulges and avoid considerable confusion.     

The apparent and intrinsic shape of the APM galaxy clusters

We estimate the distribution of intrinsic shapes of APM galaxy clusters from the distribution of their apparent shapes. We measure the projected cluster ellipticities using two alternative methods. The first method is based on moments of the discrete galaxy distribution while the second is based on moments of the smoothed galaxy distribution. We study the performance of both methods using Monte Carlo cluster simulations covering the range of APM cluster distances and including a random distribution of background galaxies. We find that the first method suffers from severe systematic biases, whereas the second is more reliable. After excluding clusters dominated by substructure and quantifying the systematic biases in our estimated shape parameters, we recover a corrected distribution of projected ellipticities. We use the non-parametric kernel method to estimate the smooth apparent ellipticity distribution, and numerically invert a set of integral equations to recover the corresponding distribution of intrinsic ellipticities under the assumption that the clusters are either oblate or prolate spheroids. The prolate spheroidal model fits the APM cluster data best.