MultiCAM: A multivariable framework for connecting the mass accretion history of haloes with their properties

TL;DR

MultiCAM introduces a multivariable framework that links the complete mass accretion history of dark matter haloes to their properties, improving prediction accuracy over traditional single-epoch models.

Contribution

It generalizes abundance matching to incorporate the full MAH, capturing complex correlations with multiple halo properties and their covariances.

Findings

Full MAH models outperform single-epoch models in predicting halo properties.

Different properties correlate with different phases of halo growth.

Extension of MultiCAM captures covariance between halo properties.

Abstract

Models that connect galaxy and halo properties often summarize a halo's mass accretion history (MAH) with a single value, and use this value as the basis for predictions. However, a single-value summary fails to capture the complexity of MAHs and information can be lost in the process. We present MultiCAM, a generalization of traditional abundance matching frameworks, which can simultaneously connect the full MAH of a halo with multiple halo and/or galaxy properties. As a first case study, we apply MultiCAM to the problem of connecting dark matter halo properties to their MAHs in the context of a dark matter-only simulation. While some halo properties, such as concentration, are more strongly correlated to the early-time mass growth of a halo, others, like the virial ratio, have stronger correlations with late-time mass growth. This highlights the necessity of considering the impact of the entire MAH on halo properties. For most of the halo properties we consider, we find that MultiCAM models that use the full MAH achieve higher accuracy than conditional abundance matching models which use a single epoch. We also demonstrate an extension of MultiCAM that captures the covariance between predicted halo properties. This extension provides a baseline model for applications where the covariance between predicted properties is important.