Minyong Jung

Nuclear star clusters (NSCs) are dense stellar systems located at the centers of galaxies. Employing Enzo- Abyss , which integrates hydrodynamics with a direct N -body solver, we introduce a simulation capable of resolving the evolution of NSCs within a live galaxy. This includes live dark matter, gaseous dynamics, star formation and feedback, and collisional dynamics for star clusters. The evolution of NSCs is typically shaped by two main processes: mergers of star clusters and in situ star formation. Our simulation enables investigation of the contributions of these mechanisms to the growth of NSCs. This work focuses on the impact of stellar physics and gas content on the growth of NSCs within a dwarf galaxy. To this end, we carry out four simulations, a fiducial simulation, one without supernova feedback, one with low star formation efficiency, and one with higher galactic gas content. This study shows a likelihood that both mergers and in situ star formation contribute to NSC evolution comparably. In addition, mergers result in disruption of dense gas clumps within star clusters, indicating that in situ star formation is suppressed when mergers occur. However, the limitations—such as the lack of individual star physics and limited spatial/particle mass resolution—hinder drawing a definite conclusion. Nevertheless, with further development, our simulations will serve as a cornerstone that untangles the complex interplay between mergers and in situ star formation in shaping the structure and mass of NSCs, thereby providing insights into their formation and evolution.

A population of dense stellar systems at high redshift has recently been uncovered by JWST. To investigate the formation of supermassive black hole (SMBH) seeds in these dense environments without invoking any ad hoc seeding mechanisms, we present star cluster-scale simulations performed with an updated version of the hydrodynamics code Enzo-Abyss , which self-consistently integrates the gravity using a direct N -body method coupled with stellar evolution. By modeling initially dense, metal-poor gas clouds with varying turbulence, we consistently find the formation of dense clusters resembling early-stage nuclear star clusters (NSCs), as well as the formation of very massive stars (VMSs) ranging from 343 M _⊙ to 5108 M _⊙ via runaway collisions, irrespective of stellar wind feedback strength. Following the direct collapse of these VMSs, the resulting intermediate-mass black holes (IMBHs) grow through Eddington-limited gas accretion and tidal disruption events (TDEs). In our most optimistic model, we find a mass accretion rate of 1.64 × 10 ⁻⁴ M _⊙ yr ⁻¹ , with TDEs contributing 23% of the total accretion over ∼10 Myr. Assuming a steady gas supply into the NSC driven by rapid structural assembly in the high-redshift environment, together with a constant TDE rate, we project that an IMBH with an initial mass of 6747 M _⊙ at the center of the NSC can grow to ∼62,000 M _⊙ within 100 Myr of its formation. Our numerical study, conducted within a single self-consistent framework that incorporates the essential physical processes, suggests that VMSs can form in dense gas clouds, collapse into IMBHs, and subsequently provide viable seeds for the SMBHs observed at high redshift.

Recent observations from the James Webb Space Telescope have revealed unexpectedly luminous galaxies, exhibiting stellar masses and luminosities significantly higher than predicted by theoretical models at Cosmic Dawn. In this study, we present a suite of cosmological zoomed-in simulations targeting high-redshift ( z ≥ 10) galaxies with dark matter halo masses in the range 10 ¹⁰ –10 ¹¹ M _⊙ at z = 10, using state-of-the-art galaxy formation simulation codes ( Enzo , Ramses , Changa , Gadget-3 , Gadget-4 , and Gizmo ). This study aims to evaluate the convergence of the participating codes and their reproducibility of high-redshift galaxies with the galaxy formation model calibrated at relatively low redshift, without additional physics for high-redshift environments. The subgrid physics follows the AGORA CosmoRun framework, with adjustments to resolution and initial conditions to emulate similar physical environments in the early Universe. The participating codes show consistent results for key galaxy properties (e.g., stellar mass), but also reveal notable differences (e.g., metallicity), indicating that galaxy properties at high redshifts are highly sensitive to the feedback implementation of the simulation. Massive halos ( M _halo ≥5 × 10 ¹⁰ M _⊙ at z = 10) succeed in reproducing observed stellar masses, metallicities, and UV luminosities at 10 ≤ z ≤ 12 without requiring additional subgrid physics, but tend to underpredict those properties at higher redshift. We also find that varying the dust-to-metal ratio modestly affects UV luminosity of simulated galaxies, whereas the absence of dust significantly enhances it. In future work, higher-resolution simulations will be conducted to better understand the formation and evolution of galaxies at Cosmic Dawn.

We investigate how differences in the stellar feedback produce disks with different morphologies in Milky Way–like progenitors over 1 ≤ z ≤ 5, using eight state-of-the-art cosmological hydrodynamics simulation codes in the AGORA project. In three of the participating codes, a distinct, rotation-dominated inner core emerges with a formation timescale of ≲300 Myr, largely driven by a major merger event, while two other codes exhibit similar signs of wet compaction—gaseous shrinkage into a compact starburst phase—at earlier epochs. The remaining three codes show only weak evidence of wet compaction. Consequently, we divide the simulated galaxies into two groups: those with strong compaction signatures and those with weaker ones. Galaxies in these two groups differ in size, stellar age gradients, and disk-to-total mass ratios. Specifically, codes with strong wet compaction build their outer disks in an inside-out fashion, leading to negative age gradients, whereas codes with weaker compaction feature flat or positive age gradients caused primarily by outward stellar migration. Although the stellar half-mass radii of these two groups diverge at z ∼ 3, the inclusion of dust extinction brings their sizes and shapes in mock observations closer to each other and to observed galaxies. We attribute the observed morphological differences primarily to variations in the stellar feedback implementations—such as delayed cooling timescales, and feedback strengths—that regulate both the onset and duration of compaction. Overall, our results suggest that disk assembly at high redshifts is highly sensitive to the details of the stellar feedback prescriptions in simulations.

Context. Satellite galaxies experience multiple physical processes when interacting with their host halos, often leading to the quenching of star formation. In the Local Group, satellite quenching has been shown to be highly efficient, affecting nearly all satellites except the most massive ones. While recent surveys study Milky Way-analogs to assess how representative our Local Group is, the dominant physical mechanisms behind satellite quenching in Milky Way-mass halos remain under debate. Aims. We analyze satellite quenching within the same Milky Way-mass halo simulated using various widely used astrophysical codes, each using different hydrodynamic methods and implementing different supernovae feedback recipes. The goal is to determine whether quenched fractions, quenching timescales, and the dominant quenching mechanisms are consistent across codes or if they show sensitivity to the specific hydrodynamic method and supernovae feedback physics employed. Methods. We used a subset of high-resolution cosmological zoom-in simulations of a Milky Way-mass halo from the multiple-code AGORA CosmoRun suite. Our analysis focuses on comparing satellite quenching across the different models and against observational data. We also analyzed the dominant mechanisms driving satellite quenching in each model. Results. We find that the quenched fraction is consistent with the latest SAGA Survey results within its 1σ host-to-host scatter across all the models. Regarding quenching timescales, all the models reproduce the trend observed in the ELVES survey, Local Group observations, and previous simulations: The less massive the satellite, the shorter its quenching timescale. All of our models converge on the dominant quenching mechanisms: Strangulation halts cold gas accretion in all satellites, while ram pressure stripping is the predominant mechanism for gas removal, and it is particularly effective in satellites with M_*8 M_⊙. Nevertheless, the efficiency of the stripping mechanisms differs among the codes, showing a strong sensitivity to the different supernovae feedback implementations and/or hydrodynamic methods employed.

We present a proof-of-concept simulation-based inference on Ω_m and σ ₈ from the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey (BOSS) LOWZ Northern Galactic Cap (NGC) catalog using neural networks and domain generalization techniques without the need of summary statistics. Using rapid light-cone simulations L-picola, mock galaxy catalogs are produced that fully incorporate the observational effects. The collection of galaxies is fed as input to a point cloud-based network, Minkowski-PointNet . We also add relatively more accurate Gadget mocks to obtain robust and generalizable neural networks. By explicitly learning the representations that reduce the discrepancies between the two different data sets via the semantic alignment loss term, we show that the latent space configuration aligns into a single plane in which the two cosmological parameters form clear axes. Consequently, during inference, the SDSS BOSS LOWZ NGC catalog maps onto the plane, demonstrating effective generalization and improving prediction accuracy compared to non-generalized models. Results from the ensemble of 25 independently trained machines find Ω_m = 0.339 ± 0.056 and σ ₈ = 0.801 ± 0.061, inferred only from the distribution of galaxies in the light-cone slices without relying on any indirect summary statistics. A single machine that best adapts to the Gadget mocks yields a tighter prediction of Ω_m = 0.282 ± 0.014 and σ ₈ = 0.786 ± 0.036. We emphasize that adaptation across multiple domains can enhance the robustness of the neural networks in observational data.

The AGORA Cosmorun (arXiv:2106.09738) is a set of hydrodynamical cosmological zoom-in simulations carried out within the AGORA High-resolution Galaxy Simulations Comparison Project (arXiv:1308.2669,arXiv:1610.03066). These simulations show the formation and evolution of a Milky Way-sized galaxy using eight of the most widely used numerical codes in the community (Art-I, Enzo, Ramses, Changa, Gadget-3, Gear, Gizmo, and Arepo). In this short report, we describe the public release of the raw output data from all of these simulations at z = 8, 7, 6, 5, 4, 3, 2 (plus at z=1, 0 when available), and several metadata files containing the halo centers, virial quantities, and merger trees. The data from even thinner timesteps will be released as soon as the upcoming collaboration papers (VII-IX) are submitted and accepted.

In this fourth paper from the AGORA Collaboration, we study the evolution down to redshift z = 2 and below of a set of cosmological zoom-in simulations of a Milky Way mass galaxy by eight of the leading hydrodynamic simulation codes. We also compare this CosmoRun suite of simulations with dark matter-only simulations by the same eight codes. We analyze general properties of the halo and galaxy at z = 4 and 3, and before the last major merger, focusing on the formation of well-defined rotationally supported disks, the mass–metallicity relation, the specific star formation rate, the gas metallicity gradients, and the nonaxisymmetric structures in the stellar disks. Codes generally converge well to the stellar-to-halo mass ratios predicted by semianalytic models at z ∼ 2. We see that almost all the hydro codes develop rotationally supported structures at low redshifts. Most agree within 0.5 dex with the observed mass–metallicity relation at high and intermediate redshifts, and reproduce the gas metallicity gradients obtained from analytical models and low-redshift observations. We confirm that the intercode differences in the halo assembly history reported in the first paper of the collaboration also exist in CosmoRun, making the code-to-code comparison more difficult. We show that such differences are mainly due to variations in code-dependent parameters that control the time stepping strategy of the gravity solver. We find that variations in the early stellar feedback can also result in differences in the timing of the low-redshift mergers. All the simulation data down to z = 2 and the auxiliary data will be made publicly available.

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 ≲0.5 dex higher stellar masses than their counterparts in the TNG100-2 (low resolution). It is also discovered that the subhalos with M _gas ∼ 10^8.5 M _⊙ in TNG100-1 have ∼0.5 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.

We analyze and compare the satellite halo populations at z ∼ 2 in the high-resolution cosmological zoom-in simulations of a 10¹² M _⊙ target halo (z = 0 mass) carried out on eight widely used astrophysical simulation codes (Art-I, Enzo, Ramses, Changa, 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. 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. When we consider only the halos containing stellar particles at z ∼ 2, the number of satellite 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. 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). We also compare other properties such as the stellar mass–halo mass relation and the mass–metallicity relation. Our work highlights the value of comparison studies such as AGORA, where outstanding problems in galaxy formation theory are studied simultaneously on multiple numerical platforms.