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On the Reliability of Merger-Trees and the Mass-Growth Histories of Dark Matter Haloes |
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| Authors: | N Hiotelis A Del Popolo |
| Institution: | 1. 1st Experimental Lyceum of Athens, Ipitou 15, Plaka, 10557, Athens, Greece 2. Roikou 17-19, Neos Kosmos, Athens, 11743, Greece 3. Physics Department, Bo?azi?i University, 80815, Bebek, Istanbul, Turkey 4. Dipartimento di Matematica, Università Statale di Bergamo, via dei Caniana, 2, 24127, Bergamo, Italy |
| Abstract: | We have used merger-trees realizations to study the formation of dark matter haloes. The construction of merger-trees is based on three different pictures about the formation of structures in the Universe. These pictures include the spherical collapse (SC), the ellipsoidal collapse (EC) and the non-radial collapse (NR). The reliability of merger-trees has been examined comparing their predictions related to the distribution of the number of progenitors, as well as the distribution of formation times, with the predictions of analytical relations. The comparison yields a very satisfactory agreement. Subsequently, the mass-growth histories (MGH) of haloes have been studied and their formation scale factors have been derived. This derivation has been based on two different definitions that are (a) the scale factor when the halo reaches half its present day mass and (b) the scale factor when the mass-growth rate falls below some specific value. Formation scale factors follow approximately power laws of mass. It has also been shown that MGHs are in good agreement with models proposed in the literature that are based on the results of N-body simulations. The agreement is found to be excellent for small haloes but, at the early epochs of the formation of large haloes, MGHs seem to be steeper than those predicted by the models based on N-body simulations. This rapid growth of mass of heavy haloes is likely to be related to a steeper central density profile indicated by the results of some N-body simulations. |
| Keywords: | Galaxies: haloes-formation-structure Methods: numerical-analytical Cosmology: dark matter |
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