4.6 Article

Spatial distribution of galactic halos and their merger histories

期刊

ASTRONOMY & ASTROPHYSICS
卷 387, 期 3, 页码 778-787

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EDP SCIENCES S A
DOI: 10.1051/0004-6361:20020339

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large-scale structure of the Universe; methods : statistical; galaxies : interactions, statistics

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We use a novel statistical tool, the mark correlation functions (MCFs), to study clustering of galaxy-size halos as a function of their properties and environment in a high-resolution numerical simulation of the LambdaCDM cosmology. We applied MCFs using several types of continuous and discrete marks: maximum circular velocity of halos, merger mark indicating whether halos experienced or not a major merger in their evolution history (the marks for halo with mergers are further split according to the epoch of the last major merger), and a stripping mark indicating whether the halo underwent a tidal stripping (i.e., mass loss). We find that halos which experienced a relatively early (z > 1) major merger or mass loss (due to tidal stripping) in their evolution histories are over-abundant in halo pairs with separations less than or similar to3 h(-1) Mpc. This result can be interpreted as spatial segregation of halos with different merger histories, qualitatively similar to the morphological segregation in the observed galaxy distribution. In addition, we find that at z = 0 the mean circular velocity of halos in pairs of halos with separations less than or similar to10 h(-1) Mpc is larger than the mean circular velocity (v) over bar (circ) of the parent halo sample. This mean circular velocity enhancement increases steadily during the evolution of halos from z = 3 to z = 0, and indicates that the luminosity dependence of galaxy clustering may be due to the mass segregation of galactic dark matter halos. The analysis presented in this paper demonstrate that MCFs provide powerful, yet algorithmically simple, quantitative measures of segregation in the spatial distribution of objects with respect to their various properties (marks). This should make MCFs very useful for analysis of spatial clustering and segregation in current and future large redshift surveys.

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