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We introduce a novel halo/galaxy matching technique between two cosmologicalsimulations with different resolutions, which utilizes the positions and massesof halos along their subhalo merger tree. With this tool, we conduct a study ofresolution biases through the galaxy-by-galaxy inspection of a pair ofsimulations that have the same simulation configuration but different massresolutions, utilizing a suite of IllustrisTNG simulations to assess the impacton galaxy properties. We find that, with the subgrid physics model calibratedfor TNG100-1, subhalos in TNG100-1 (high resolution) have $\lesssim0.5$ dexhigher stellar masses than their counterparts in the TNG100-2 (low-resolution).It is also discovered that the subhalos with $M_{\mathrm{gas}}\sim10^{8.5}{\rmM}_\odot$ in TNG100-1 have $\sim0.5$ dex higher gas mass than those inTNG100-2. The mass profiles of the subhalos reveal that the dark matter massesof low-resolution subhalos are $\sim0.6$ times lower within 2 kpc, near theresolution limit. The differences in stellar mass and hot gas mass are mostpronounced in the central region. We exploit machine learning to build acorrection mapping for the physical quantities of subhalos from low- tohigh-resolution simulations (TNG300-1 and TNG100-1), which enables us to findan efficient way to compile a high-resolution galaxy catalog even from alow-resolution simulation. Our tools can easily be applied to other largecosmological simulations, testing and mitigating the resolution biases of theirnumerical codes and subgrid physics models.

Merger Tree-based Halo/Galaxy Matching Between Cosmological Simulations  with Different Resolutions: Galaxy-by-galaxy Resolution Study and the Machine  Learning-based Correction