A Stochastic Theory of the Hierarchical Clustering II. Halo progenitor mass function and large-scale bias
Andrea Lapi, Luigi Danese

TL;DR
This paper extends a stochastic model of hierarchical clustering to accurately predict halo progenitor mass functions and large-scale bias, aligning well with N-body simulation results across cosmic time.
Contribution
It introduces a stochastic differential equation with a mass-dependent collapse threshold, improving predictions of progenitor distributions and bias in hierarchical clustering models.
Findings
Exact solution for progenitor mass function matching simulations
Analytical model for large-scale halo bias in agreement with N-body results
Demonstrates the stochastic theory's effectiveness in describing halo evolution
Abstract
We generalize the stochastic theory of hierarchical clustering presented in paper I by Lapi & Danese (2020) to derive the (conditional) halo progenitor mass function and the related large-scale bias. Specifically, we present a stochastic differential equation that describes fluctuations in the mass growth of progenitor halos of given descendant mass and redshift, as driven by a multiplicative Gaussian white noise involving the power spectrum and the spherical collapse threshold of density perturbations. We demonstrate that, as cosmic time passes, the noise yields an average drift of the progenitors toward larger masses, that quantitatively renders the expectation from the standard extended Press & Schechter (EPS) theory. We solve the Fokker-Planck equation associated to the stochastic dynamics, and obtain as an exact, stationary solution the EPS progenitor mass function. Then we…
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