We present an algorithm for generating merger histories of dark matter haloes. The algorithm is based on the excursion-set approach with moving barriers whose shape is motivated by the ellipsoidal collapse model of halo formation. In contrast to most other merger-tree algorithms, ours takes discrete steps in mass rather than time. This allows us to quantify effects which arise from the fact that outputs from numerical simulations are usually in discrete time bins. In addition, it suggests a natural set of scaling variables for describing the abundance of halo progenitors; this scaling is not as general as that associated with a spherical collapse. We test our algorithm by comparing its predictions with measurements in numerical simulations. The progenitor mass fractions and mass functions are in good agreement, as is the predicted scaling law. We also test the formation-redshift distribution, the mass distribution at formation, and the redshift distribution of the most recent major merger; all are in reasonable agreement with N-body simulation data, over a broad range of masses and redshifts. Finally, we study the effects of sampling in discrete time snapshots. In all cases, the improvement over algorithms based on the spherical collapse assumption is significant.

Merger history trees of dark matter haloes in moving barrier models

GIOCOLI, CARLO;
2008

Abstract

We present an algorithm for generating merger histories of dark matter haloes. The algorithm is based on the excursion-set approach with moving barriers whose shape is motivated by the ellipsoidal collapse model of halo formation. In contrast to most other merger-tree algorithms, ours takes discrete steps in mass rather than time. This allows us to quantify effects which arise from the fact that outputs from numerical simulations are usually in discrete time bins. In addition, it suggests a natural set of scaling variables for describing the abundance of halo progenitors; this scaling is not as general as that associated with a spherical collapse. We test our algorithm by comparing its predictions with measurements in numerical simulations. The progenitor mass fractions and mass functions are in good agreement, as is the predicted scaling law. We also test the formation-redshift distribution, the mass distribution at formation, and the redshift distribution of the most recent major merger; all are in reasonable agreement with N-body simulation data, over a broad range of masses and redshifts. Finally, we study the effects of sampling in discrete time snapshots. In all cases, the improvement over algorithms based on the spherical collapse assumption is significant.
2008
Jorge Moreno;Carlo Giocoli;Ravi K. Sheth
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/143321
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