Merger Tree-based Galaxy Matching: A Comparative Study Across Different Resolutions

TL;DR

This study introduces a new galaxy matching technique across different simulation resolutions, analyzes resolution biases in galaxy properties, and employs machine learning to correct low-resolution data, improving galaxy catalog accuracy.

Contribution

A novel merger tree-based matching method and machine learning correction approach for resolution biases in cosmological simulations.

Findings

Subhalos in high-resolution simulations have higher stellar and gas masses.

Dark matter mass profiles converge well except near the resolution limit.

Machine learning effectively corrects low-resolution galaxy properties.

Abstract

We introduce a novel halo/galaxy matching technique between two cosmological simulations with different resolutions, which utilizes the positions and masses of halos along their subhalo merger tree. With this tool, we conduct a study of resolution biases through the {\it galaxy-by-galaxy} inspection of a pair of simulations that have the same simulation configuration but different mass resolutions, utilizing a suite of {\sc IllustrisTNG} simulations to assess the impact on galaxy properties. We find that, with the subgrid physics model calibrated for TNG100-1, subhalos in TNG100-1 (high resolution) have $\lesssim0.5$ dex higher stellar masses than their counterparts in the TNG100-2 (low-resolution). It is also discovered that the subhalos with $M_{\mathrm{gas}}\sim10^{8.5}\,{\rm M}_\odot$ in TNG100-1 have $\sim0.5$ dex higher gas mass than those in TNG100-2. The mass profiles of the subhalos reveal that the dark matter masses of subhalos in TNG100-2 converge well with those from TNG100-1, except within 4 kpc of the resolution limit. The differences in stellar mass and hot gas mass are most pronounced in the central region. We exploit machine learning to build a correction mapping for the physical quantities of subhalos from low- to high-resolution simulations (TNG300-1 and TNG100-1), which enables us to find an efficient way to compile a high-resolution galaxy catalog even from a low-resolution simulation. Our tools can easily be applied to other large cosmological simulations, testing and mitigating the resolution biases of their numerical codes and subgrid physics models.