..

Research

Dark Matter Deficient Galaxies Produced via High-velocity Galaxy Collisions in High-resolution Numerical Simulations

Eun-jin Shin¹, Minyong Jung¹, Goojin Kwon¹, Ji-hoon Kim, Joohyun Lee, Yongseok Jo, and Boon Kiat Oh

[ADS] The recent discovery of diffuse dwarf galaxies that are deficient in dark matter appears to challenge the current paradigm of structure formation in our Universe. We describe the numerical experiments to determine if the so-called dark matter deficient galaxies (DMDGs) could be produced when two gas-rich, dwarf-sized galaxies collide with a high relative velocity of ∼300kms−1. Using idealized high-resolution simulations with both mesh-based and particle-based gravito-hydrodynamics codes, we find that DMDGs can form as high-velocity galaxy collisions separate dark matter from the warm disk gas which subsequently is compressed by shock and tidal interaction to form stars. Then using a large simulated universe IllustrisTNG, we discover a number of high-velocity galaxy collision events in which DMDGs are expected to form. However, we did not find evidence that these types of collisions actually produced DMDGs in the TNG100-1 run. We argue that the resolution of the numerical experiment is critical to realize the “collision-induced” DMDG formation scenario. Our results demonstrate one of many routes in which galaxies could form with unconventional dark matter fractions.

The AGORA High-resolution Galaxy Simulations Comparison Project. V: Satellite Galaxy Populations In A Cosmological Zoom-in Simulation of A Milky Way-mass Halo

Minyong Jung, Santi Roca-Fabrega, Ji-hoon Kim, and 18 coauthors in the AGORA collaboration.

[ADS] We analyze and compare the satellite halo populations at z~2 in the high-resolution cosmological zoom-in simulations of a 10^12 Msun target halo (z=0 mass) carried out on eight widely-used astrophysical simulation codes (Art-I, Enzo, Ramses, Changa-T, Gadget-3, Gear, Arepo-T, and Gizmo) for the AGORA High-resolution Galaxy Simulations Comparison Project. We use slightly different redshift epochs near z=2 for each code (hereafter “z~2”) at which the eight simulations are in the same stage in the target halo’s merger history. We also study the dark matter-only (DMO) simulations with the same cosmological initial condition to isolate the effect of baryonic physics.
We find that the number of satellite halos at z~2 in all participating AGORA hydrodynamic simulations (“CosmoRun”) is fewer than its counterpart in the DMO runs.
When we consider only the halos containing stellar particles at z~2, the number of satellite {\it galaxies} is significantly fewer than that of dark matter halos in all participating AGORA simulations, and is comparable to the number of present-day satellites near the Milky Way or M31.
This difference between the hydrodynamic simulations and the DMO simulations can be attributed to various baryonic effects.
After identifying the matched pairs of halos between the CosmoRun simulations and the DMO simulations, we discover that each CosmoRun halo tends to be less massive than its DMO counterpart. The so-called “missing satellite problem” is fully resolved across all participating codes simply by implementing the common baryonic physics adopted in AGORA and the stellar feedback prescription commonly used in each code, with sufficient numerical resolution (<100 proper pc at z=2).
This marks the first time that different codes have converged on a common conclusion for this issue. We also find reasonable inter-code agreement in other properties of satellite galaxies such as the stellar mass$-$halo mass relation and the mass-metallicity relation.

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

Minyong Jung, Ji-hoon Kim, Boon Kiat Oh, Sungwook E. Hong, Jaehyun Lee, Juhan Kim

[ADS] 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 galaxy-by-galaxy inspection of a pair of simulations that have the same simulation configuration but different mass resolutions, utilizing a suite of 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 $\lesssim 0.5$ dex higher stellar masses than their counterparts in the TNG100-2 (low-resolution). It is also discovered that the subhalos with $M_{\rm gas} \sim 10^{8.5}\,{\rm M}_\odot$ in TNG100-1 have $\sim 0.5$ dex higher gas mass than those in TNG100-2. The mass profiles of the subhalos reveal that the dark matter masses of low-resolution subhalos are $\sim0.6$ times lower within 2 kpc, near 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.