Mass functions and bias of dark matter halos

TL;DR

This paper analytically derives the large-mass tail of dark matter halo mass functions and bias, confirming results with simulations and improving existing models to better match observed behaviors across scales and redshifts.

Contribution

It provides exact analytical results for the rare massive halo tail, revises the bias derivation including halo motions, and offers a fitting formula consistent with simulations.

Findings

Analytical large-mass cutoff matches simulations.

New bias formula agrees with numerical data.

Improved bias model captures redshift-dependent steepening.

Abstract

We revisit the study of the mass functions and the bias of dark matter halos. Focusing on the limit of rare massive halos, we point out that exact analytical results can be obtained for the large-mass tail of the halo mass function. This is most easily seen from a steepest-descent approach, that becomes asymptotically exact for rare events. We also revisit the traditional derivation of the bias of massive halos, associated with overdense regions in the primordial density field. We check that the theoretical large-mass cutoff agrees with the mass functions measured in numerical simulations. For halos defined by a nonlinear threshold $\delta=200$ this corresponds to using a linear threshold $\delta_L\simeq 1.59$ instead of the traditional value $\simeq 1.686$. We also provide a fitting formula that matches simulations over all mass scales and obeys the exact large-mass tail. Next, paying attention to the Lagrangian-Eulerian mapping (i.e. corrections associated with the motions of halos), we improve the standard analytical formula for the bias of massive halos. We check that our prediction, which contains no free parameter, agrees reasonably well with numerical simulations. In particular, it recovers the steepening of the dependence on scale of the bias that is observed at higher redshifts, which published fitting formulae did not capture. This behavior mostly arises from nonlinear biasing.