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- W4386977255 abstract "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." @default.
- W4386977255 created "2023-09-23" @default.
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- W4386977255 date "2023-09-20" @default.
- W4386977255 modified "2023-09-27" @default.
- W4386977255 title "Estimating major merger rates and spin parameters ab initio via the clustering of critical events" @default.
- W4386977255 doi "https://doi.org/10.48550/arxiv.2309.11558" @default.
- W4386977255 hasPublicationYear "2023" @default.
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