A Stochastic Theory of the Hierarchical Clustering II. Halo progenitor mass function and large-scale bias

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 introduce a modification of the stochastic equation in terms of a mass-dependent collapse threshold modulating the noise, and solve analytically the associated Fokker-Planck equation for the progenitor mass function. The latter is found to be in excellent agreement with the outcomes of $N-$body simulations; even more remarkably, this is achieved with the same shape of the collapse threshold used in paper I to reproduce the halo mass function. Finally, we exploit the above results to compute the large-scale halo bias, and find it in pleasing agreement with the $N-$body outcomes. All in all, the present paper illustrates that the stochastic theory of hierarchical clustering introduced in paper I can describe effectively not only halos' abundance, but also their progenitor distribution and their correlation with the large-scale environment across cosmic times.