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Although coherent large-scale structures such as filaments and walls are apparent to the eye in galaxy redshift surveys, they have so far proven difficult to characterize with computer algorithms. This paper presents a procedure that uses the eigenvalues and eigenvectors of the Hessian matrix of the galaxy density field to characterize the morphology of large-scale structure. By analysing the smoothed density field and its Hessian matrix, we can determine the types of structure - walls, filaments, or clumps - that dominate the large-scale distribution of galaxies as a function of scale. We have run the algorithm on mock galaxy distributions in a LCDM cosmological N-body simulation and the observed galaxy distributions in the Sloan Digital Sky Survey. The morphology of structure is similar between the two catalogues, both being filament-dominated on 10-20 h^{-1} Mpc smoothing scales and clump-dominated on 5 h^{-1} Mpc scales. There is evidence for walls in both distributions, but walls are not the dominant structures on scales smaller than ~25 h^{-1} Mpc. Analysis of the simulation suggests that, on a given comoving smoothing scale, structures evolve with time from walls to filaments to clumps, where those found on smaller smoothing scales are further in this progression at a given time.
In this study, we probe the transition to cosmic homogeneity in the Large Scale Structure (LSS) of the Universe using the CMASS galaxy sample of BOSS spectroscopic survey which covers the largest effective volume to date, $3 h^{-3} mathrm{Gpc}^3$ at
Many statistical methods have been proposed in the last years for analyzing the spatial distribution of galaxies. Very few of them, however, can handle properly the border effects of complex observational sample volumes. In this paper, we first show
We present a detailed strong lensing, weak lensing and X-ray analysis of Abell 2744 (z = 0.308), one of the most actively merging galaxy clusters known. It appears to have unleashed `dark, `ghost, `bullet and `stripped substructures, each ~10^14 sola
Galaxies grow primarily via accretion-driven star formation in discs and merger-driven growth of bulges. These processes are implicit in semi-analytical models of galaxy formation, with bulge growth in particular relating directly to the hierarchical
We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that includes: (