Characterizing the assembly of dark matter halos with protohalo size histories: I. Redshift evolution, relation to descendant halos, and halo assembly bias

TL;DR

This paper introduces a new method to analyze dark matter halo assembly histories through protohalo size evolution, revealing strong correlations with galaxy properties and halo bias, and offering observational links across cosmic time.

Contribution

It presents a novel protohalo size history approach that better captures halo assembly bias and links progenitors to descendants than traditional mass accretion metrics.

Findings

Protohalo size history follows a double power-law function.

Amplitude correlates with central-to-total stellar mass ratios.

Protohalo size history induces a strong assembly bias effect.

Abstract

We propose a novel method to quantify the assembly histories of dark matter halos with the redshift evolution of the mass-weighted spatial variance of their progenitor halos, i.e. the protohalo size history. We find that the protohalo size history for each individual halo at z~0 can be described by a double power-law function. The amplitude of the fitting function strongly correlates to the central-to-total stellar mass ratios of descendant halos. The variation of the amplitude of the protohalo size history can induce a strong halo assembly bias effect for massive halos. This effect is detectable in observation using the central-to-total stellar mass ratio as a proxy of the protohalo size. The correlation to the descendant central-to-total stellar mass ratio and the halo assembly bias effect seen in the protohalo size are much stronger than that seen in the commonly adopted half-mass formation time derived from the mass accretion history. This indicates that the information loss caused by the compression of halo merger trees to mass accretion histories can be captured by the protohalo size history. Protohalo size thus provides a useful quantity to connect protoclusters across cosmic time and to link protoclusters with their descendant clusters in observations.