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In order to attain a statistical description of the evolution of cosmic density fluctuations in agreement with results from the numerical simulations, we introduce a probability conditional formalism (CF) based on an inventory of isolated overdense regions in a density random field. This formalism is a useful tool for describing at the same time the mass function (MF) of dark haloes, their mass aggregation histories (MAHs) and merging rates (MRs). The CF focuses on virialized regions in a self-consistent way rather than in mass elements, and it offers an economical description for a variety of random fields. Within the framework of the CF, we confirm that, for a Gaussian field, it is not possible to reproduce at the same time the MF, MAH, and MR of haloes, both for a constant and moving barrier. Then, we develop an inductive method for constraining the cumulative conditional probability from a given halo MF description, and thus, using the CF, we calculate the halo MAHs and MRs. By applying this method to the MF measured in numerical simulations by Tinker et al. 2008, we find that a reasonable solution, justified by a mass conservation argument, is obtained if ones introduce a rescaling -increment by ~30% - of the virial mass used in simulations and a (slight) deviation from Gaussianity. Thus, both the MAH and MR obtained by a Monte Carlo merger tree agree now with the predictions of numerical simulations. We discuss on the necessity of rescaling the virial mass in simulations when comparing with analytical approaches on the ground of the matter not accounted as part of the halos and the halo mass limit due to numerical. Our analysis supports the presence of a diffuse dark matter component that is not taken into account in the measured halo MFs inasmuch as it is not part of the collapsed structures.
The mass function of dark halos in a Lambda-dominated cold dark matter (LambdaCDM) universe is investigated. 529 output files from five runs of N-body simulations are analyzed using the friends-of-friends cluster finding algorithm. All the runs use 5
We compare the predicted conditional mass function (CMF) of dark matter halos from two theoretical prescriptions against numerical N-body simulations, both in overdense and underdense regions and at different Eulerian scales ranging from $5$ to $30,h
We use a large $N$-body simulation to study the relation of the structural properties of dark matter halos to their assembly history and environment. The complexity of individual halo assembly histories can be well described by a small number of prin
We use the forward modeling approach to galaxy clustering combined with the likelihood from the effective-field theory of large-scale structure to measure assembly bias, i.e. the dependence of halo bias on properties beyond the total mass, in the lin
We present a new empirical model for the mass assembly of dark matter halos. We approximate the growth of individual halos as a simple power-law function of time, where the power-law index smoothly decreases as the halo transitions from the fast-accr