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Pré-Publication, Document De Travail Année : 2023

Estimating major merger rates and spin parameters ab initio via the clustering of critical events

Résumé

We build a model to predict from first principles the properties of major mergers. We predict these from the coalescence of peaks and saddle points in the vicinity of a given larger peak, as one increases the smoothing scale in the initial linear density field as a proxy for cosmic time. To refine our results, we also ensure, using a suite of $\sim 400$ power-law Gaussian random fields smoothed at $\sim 30$ different scales, that the relevant peaks and saddles are topologically connected: they should belong to a persistent pair before coalescence. Our model allows us to (a) compute the probability distribution function of the satellite-merger separation in Lagrangian space: they peak at three times the smoothing scale; (b) predict the distribution of the number of mergers as a function of peak rarity: halos typically undergo two major mergers ($>$1:10) per decade of mass growth; (c) recover that the typical spin brought by mergers: it is of the order of a few tens of per cent.

Dates et versions

hal-04246812 , version 1 (17-10-2023)

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Corentin Cadiou, Eric Pichon-Pharabod, Christophe Pichon, Dmitri Pogosyan. Estimating major merger rates and spin parameters ab initio via the clustering of critical events. 2023. ⟨hal-04246812⟩
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