Halo abundances and counts-in-cells: The excursion set approach with correlated steps

TL;DR

This paper refines the excursion set approach for predicting halo properties by incorporating correlated steps in random walks, extending previous models to include moving barriers and constraints, with validation against Monte Carlo simulations.

Contribution

It introduces an improved approximation for correlated steps in the excursion set approach, extending it to moving barriers and constrained walks, enhancing predictive accuracy.

Findings

The approximation aligns well with Monte Carlo solutions.

Extension to moving barriers improves modeling flexibility.

Conditional distributions are accurately described by a simple rescaling.

Abstract

The Excursion Set approach has been used to make predictions for a number of interesting quantities in studies of nonlinear hierarchical clustering. These include the halo mass function, halo merger rates, halo formation times and masses, halo clustering, analogous quantities for voids, and the distribution of dark matter counts in randomly placed cells. The approach assumes that all these quantities can be mapped to problems involving the first crossing distribution of a suitably chosen barrier by random walks. Most analytic expressions for these distributions ignore the fact that, although different k-modes in the initial Gaussian field are uncorrelated, this is not true in real space: the values of the density field at a given spatial position, when smoothed on different real-space scales, are correlated in a nontrivial way. As a result, the problem is to estimate first crossing distribution by random walks having correlated rather than uncorrelated steps. In 1990, Peacock & Heavens presented a simple approximation for the first crossing distribution of a single barrier of constant height by walks with correlated steps. We show that their approximation can be thought of as a correction to the distribution associated with what we call smooth completely correlated walks. We then use this insight to extend their approach to treat moving barriers, as well as walks that are constrained to pass through a certain point before crossing the barrier. For the latter, we show that a simple rescaling, inspired by bivariate Gaussian statistics, of the unconditional first crossing distribution, accurately describes the conditional distribution, independently of the choice of analytical prescription for the former. In all cases, comparison with Monte-Carlo solutions of the problem shows reasonably good agreement. (Abridged)