The Mass Function of Unprocessed Dark Matter Halos and Merger Tree Branching Rates

TL;DR

This paper refines the modeling of dark matter halo merger histories by removing double-counting biases and accounting for measurement errors, leading to more accurate predictions aligned with high-precision N-body simulations.

Contribution

It introduces a method to measure halo mass functions and merger rates without double-counting and incorporates measurement errors into the analysis.

Findings

Double-counting of halos biases merger rate estimates.

Accounting for measurement errors reduces systematic biases.

Results align more closely with high-precision N-body simulations.

Abstract

A common approach in semi-analytic modeling of galaxy formation is to construct Monte Carlo realizations of merger histories of dark matter halos whose masses are sampled from a halo mass function. Both the mass function itself, and the merger rates used to construct merging histories are calibrated to N-body simulations. Typically, "backsplash" halos (those which were once subhalos within a larger halo, but which have since moved outside of the halo) are counted in both the halo mass function, and in the merger rates (or, equivalently, progenitor mass functions). This leads to a double-counting of mass in Monte Carlo merger histories which will bias results relative to N-body results. We measure halo mass functions and merger rates with this double-counting removed in a large, cosmological N-body simulation with cosmological parameters consistent with current constraints. Furthermore, we account for the inherently noisy nature of N-body halo mass estimates when fitting functions to N-body data, and show that ignoring these errors leads to a significant systematic bias given the precision statistics available from state-of-the-art N-body cosmological simulations.