arXiv subjects

Greg L. Bryan

Publications and source records attributed to Greg L. Bryan.

At least 55 records · Page 3Linked to original sources

arXiv2025

How does feedback affect the star formation histories of galaxies?

Star formation in galaxies is regulated by the interplay of a range of processes that shape the multiphase gas in the interstellar and circumgalactic media. Using the CAMELS suite of cosmological simulations, we study the effects of varying feedback and cosmology on the average star formation histories (SFHs) of galaxies at $z\sim0$ across the IllustrisTNG, SIMBA and ASTRID galaxy formation models. We find that galaxy SFHs in all three models are sensitive to changes in stellar feedback, which affects the efficiency of baryon cycling and the rates at which central black holes grow, while effects of varying AGN feedback depend on model-dependent implementations of black hole seeding, accretion and feedback. We also find strong interaction terms that couple stellar and AGN feedback, usually by regulating the amount of gas available for the central black hole to accrete. Using a double power-law to describe the average SFHs, we derive a general set of equations relating the shape of the SFHs to physical quantities like baryon fraction and black hole mass across all three models. We find that a single set of equations (albeit with different coefficients) can describe the SFHs across all three CAMELS models, with cosmology dominating the SFH at early times, followed by halo accretion, and feedback and baryon cycling at late times. Galaxy SFHs provide a novel, complementary probe to constrain cosmology and feedback, and can connect the observational constraints from current and upcoming galaxy surveys with the physical mechanisms responsible for regulating galaxy growth and quenching.

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arXiv2025

Field-Level Comparison and Robustness Analysis of Cosmological N-body Simulations

We present the first field-level comparison of cosmological N-body simulations, considering various widely used codes: Abacus, CUBEP$^3$M, Enzo, Gadget, Gizmo, PKDGrav, and Ramses. Unlike previous comparisons focused on summary statistics, we conduct a comprehensive field-level analysis: evaluating statistical similarity, quantifying implications for cosmological parameter inference, and identifying the regimes in which simulations are consistent. We begin with a traditional comparison using the power spectrum, cross-correlation coefficient, and visual inspection of the matter field. We follow this with a statistical out-of-distribution (OOD) analysis to quantify distributional differences between simulations, revealing insights not captured by the traditional metrics. We then perform field-level simulation-based inference (SBI) using convolutional neural networks (CNNs), training on one simulation and testing on others, including a full hydrodynamic simulation for comparison. We identify several causes of OOD behavior and biased inference, finding that resolution effects, such as those arising from adaptive mesh refinement (AMR), have a significant impact. Models trained on non-AMR simulations fail catastrophically when evaluated on AMR simulations, introducing larger biases than those from hydrodynamic effects. Differences in resolution, even when using the same N-body code, likewise lead to biased inference. We attribute these failures to a CNN's sensitivity to small-scale fluctuations, particularly in voids and filaments, and demonstrate that appropriate smoothing brings the simulations into statistical agreement. Our findings motivate the need for careful data filtering and the use of field-level OOD metrics, such as PQMass, to ensure robust inference.

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arXiv2025

Aeos: Transport of Metals from Minihalos following Population III Stellar Feedback

We investigate how stellar feedback from the first stars (Population III) distributes metals through the interstellar and intergalactic medium using the star-by-star cosmological hydrodynamics simulation, Aeos. We find that energy injected from the supernovae of the first stars is enough to expel a majority of gas and injected metals beyond the virial radius of halos with mass $M_* \lesssim 10^7$ M$_\odot$, regardless of the number of supernovae. This prevents self-enrichment and results in a non-monotonic increase in metallicity at early times. Most minihalos ($M \gtrsim 10^5 \, \rm M_\odot$) do not retain significant fractions of the yields produced within their virial radii until they have grown to halo masses of $M \gtrsim 10^7 \, \rm M_\odot$. The loss of metals to regions well beyond the virial radius delays the onset of enriched star formation and extends the period that Population III star formation can persist. We also explore the contributions of different nucleosynthetic channels to 10 individual elements. On the timescale of the simulation (lowest redshift $z=14.3$), enrichment is dominated by core-collapse supernovae for all elements, but with a significant contribution from asymptotic giant branch winds to the s-process elements, which are normally thought to only be important at late times. In this work, we establish important mechanisms for early chemical enrichment which allows us to apply Aeos in later epochs to trace the evolution of enrichment during the complete transition from Population III to Population II stars.

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arXiv2025

Simulating High-Velocity Clouds in the Observational Plane: An Initial Study with the Smith Cloud

High-velocity clouds (HVCs) may fuel future star formation in the Milky Way, but they must first survive their passage through the hot halo. While recent work has improved our understanding of the survival criterion for cloud-wind interactions, few observational comparisons exist that test this criterion. We therefore present an initial comparison of simulations with the Smith Cloud (SC; $d=$ 12.4 kpc, $l, b = 40^{\circ}, -13^{\circ}$) as mapped with the GALFA-HI survey. We use the Smith Cloud's observed properties to motivate simulations of comparable clouds in wind tunnel simulations with Enzo-E, an MHD code. For both observations and simulations, we generate moment maps, characterize turbulence through a projected first-order velocity structure function (VSF), and do the same for HI column density with a normalized autocovariance function. We explore how initial cloud conditions (such as radius, metallicity, thermal pressure, viewing angle, and distance) affect these statistics, demonstrating that the small-scale VSF is sensitive to cloud turbulence while large scales depend on cloud bulk velocity and viewing angle. We find that some simulations reproduce key observational features (particularly the correlation between column density and velocity dispersion) but none match all observational probes at the same time (the large scales of the column density autocovariance is particularly challenging). We find that the simulated cloud (cloud C) showing growth via a turbulent radiative mixing layer (TRML) is the best match, implying the importance of TRML-mediated cooling for Milky Way HVCs. We conclude by suggesting improvements for simulations to better match observed HVCs.

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arXiv2025

The Co-Evolution of Stellar Wind-blown Bubbles and Photoionized Gas I: Physical Principles and a Semi-Analytic Model

We propose a new framework for the simultaneous feedback of stellar winds and photo-ionizing radiation from massive stars, distinguishing the locations where forces are applied, and consequences for internal spatio-temporal evolution of the whole feedback bubble (FB). We quantify the relative dynamical importance of wind-blown bubbles (WBB) versus the photoionized region (PIR) by the ratio of the radius at which the WBB is in pressure equilibrium with the PIR, $R_{\rm eq}$, to the Strömgren radius, $R_{\rm St}$. $ζ\equiv R_{\rm eq}/R_{\rm St}$ quantifies the dynamical dominance of WBBs ($ζ> 1$) or the PIR ($ζ< 1$). We calculate $ζ$ and find that, for momentum-driven winds, $0.1 \lesssim ζ\lesssim 1$ for the star-forming regions in (i) typical Milky Way-like giant molecular clouds (GMCs), (ii) the most massive of individual OB stars, and (iii) dense, low-metallicity environments, relevant in the early universe. In this regime, both WBBs and the PIR are dynamically important to the expansion of the FB. We develop a semi-analytic Co-Evolution Model (CEM) that takes into account the spatial distribution of forces and the back reactions of both the WBB and PIR. In the $ζ<1$ regime where the CEM is most relevant, the model differs in the total FB momentum by up to 25% compared to naive predictions. In the weak-wind limit of $ζ\ll 1$, applicable to individual OB stars or low-mass clusters, the CEM has factors $\gtrsim 2$ differences in WBB properties. In a companion paper we compare these models to three-dimensional, turbulent hydro-dynamical simulations.

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arXiv2025

The Co-Evolution of Stellar Wind-blown Bubbles and Photoionized Gas II: 3D RMHD Simulations and Tests of Semi-Analytic Models

In a companion paper (Paper I) we presented a Co-Evolution Model (CEM) in which to consider the evolution of feedback bubbles driven by massive stars through both stellar winds and ionizing radiation, outlining when either of these effects is dominant and providing a model for how they evolve together. Here we present results from three-dimensional radiation magneto-hydrodynamical (RMHD) simulations of this scenario for parameters typical of massive star-forming clouds in the Milky Way: precisely the regime where we expect both feedback mechanisms to matter. While we find that the CEM agrees with the simulations to within 25% for key parameters and modestly outperforms previous idealized models, disagreements remain. We show that these deviations originate mainly from the CEM's lack of (i) background inhomogeneity caused by turbulence and (ii) time-variable momentum enhancements in the wind-blown bubble (WBB). Additionally, we find that photoionized gas acts similarly to magnetic fields ([as in Lancaster et al. 2024a) by decreasing the WBB's surface area. This causes a decrease in the amount of cooling at the WBB's interface, resulting in an enhanced WBB dynamical impact.

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arXiv2025

The Effects of AGN Feedback on the Lyman-$α$ Forest Flux Power Spectrum

We study the effects of AGN feedback on the Lyman-$α$ forest 1D flux power spectrum (P1D). Using the Simba cosmological-hydrodynamic simulations, we examine the impact that adding different AGN feedback modes has on the predicted P1D. We find that, for Simba, the impact of AGN feedback is most dramatic at lower redshifts ($z<1$) and that AGN jet feedback plays the most significant role in altering the P1D. The effects of AGN feedback can be seen across a large range of wavenumbers ($1.5\times10^{-3} 2.0$), AGN feedback has a $2\%$ effect on the P1D for $k<5\times10^{-2}$ s/km and an $8\%$ effect for $k>5\times10^{-2}$ s/km. We show that the small scale effect is reduced when normalizing the simulation to the observed mean flux. On large scales, the effect of AGN feedback appears via a change in the IGM temperature and is thus unlikely to bias cosmological parameters. The strong AGN jets in the Simba simulation can reproduce the $z>2$ Lyman-$α$ forest. We stress that analyses comparing different AGN feedback models to future higher precision data will be necessary to determine the full extent of this effect.

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arXiv2025

Learning the Universe: $3\ h^{-1}{\rm Gpc}$ Tests of a Field Level $N$-body Simulation Emulator

We apply and test a field-level emulator for non-linear cosmic structure formation in a volume matching next-generation surveys. Inferring the cosmological parameters and initial conditions from which the particular galaxy distribution of our Universe was seeded can be achieved by comparing simulated data to observational data. Previous work has focused on building accelerated forward models that efficiently mimic these simulations. One of these accelerated forward models uses machine learning to apply a non-linear correction to the linear $z=0$ Zeldovich approximation (ZA) fields, closely matching the cosmological statistics in the $N$-body simulation. This emulator was trained and tested at $(h^{-1}{\rm Gpc})^3$ volumes, although cosmological inference requires significantly larger volumes. We test this emulator at $(3\ h^{-1}{\rm Gpc})^3$ by comparing emulator outputs to $N$-body simulations for eight unique cosmologies. We consider several summary statistics, applied to both the raw particle fields and the dark matter (DM) haloes. We find that the power spectrum, bispectrum and wavelet statistics of the raw particle fields agree with the $N$-body simulations within ${\sim} 5 \%$ at most scales. For the haloes, we find a similar agreement between the emulator and the $N$-body for power spectrum and bispectrum, though a comparison of the stacked profiles of haloes shows that the emulator has slight errors in the positions of particles in the highly non-linear interior of the halo. At these large $(3\ h^{-1}{\rm Gpc})^3$ volumes, the emulator can create $z=0$ particle fields in a thousandth of the time required for $N$-body simulations and will be a useful tool for large-scale cosmological inference. This is a Learning the Universe publication.

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arXiv2025

Applying a star formation model calibrated on high-resolution interstellar medium simulations to cosmological simulations of galaxy formation

Modern high-resolution simulations of the interstellar medium (ISM) have shown that key factors in governing star formation are the competing influences of radiative dissipation, pressure support driven by stellar feedback, and the relentless pull of gravity. Cosmological simulations of galaxy formation, such as IllustrisTNG or ASTRID, are however not able to resolve this physics in detail and therefore need to rely on approximate treatments. These have often taken the form of empirical subgrid models of the ISM expressed in terms of an effective equation of state (EOS) that relates the mean ISM pressure to the mean gas density. Here we seek to improve these heuristic models by directly fitting their key ingredients to results of the high-resolution TIGRESS simulations, which have shown that the dynamical equilibrium of the ISM can be understood in terms of a pressure-regulated, feedback modulated (PRFM) model for star formation. Here we explore a simple subgrid model that draws on the PRFM concept but uses only local quantities. It accurately reproduces PRFM for pure gas disks, while it predicts slightly less star formation than PRFM in the presence of an additional thin stellar disk. We compare the properties of this model with the older Springel and Hernquist and TNG prescriptions, and apply all three to isolated simulations of disk galaxies as well as to a set of high-resolution zoom-in simulations carried out with a novel 'multi-zoom' technique that we introduce in this study. The softer EOS implied by TIGRESS produces substantially thinner disk galaxies, which has important ramifications for disk stability and galaxy morphology. The total stellar mass of galaxies is however hardly modified at low redshift, reflecting the dominating influence of large-scale gaseous inflows and outflows to galaxies, which are not sensitive to the EOS itself

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arXiv2025

Modeling Cosmic Rays at AGN Jet-Driven Shock Fronts

Active Galactic Nuclei (AGN) feedback is a key physical mechanism proposed to regulate star formation, primarily in massive galaxies. In particular, cosmic rays associated with AGN jets have the potential to efficiently suppress cooling flows and quench star formation. The locus of cosmic ray production and their coupling to gas play a crucial role in the overall self-regulation process. To investigate this in detail, we conduct high-resolution, non-cosmological MHD simulations of a massive $10^{14} {\rm M_\odot}$ halo using the FIRE-2 (Feedback In Realistic Environments) stellar feedback model. We explore a variety of AGN jet feedback scenarios with cosmic rays, examining different values for the cosmic ray energy fraction in jets, cosmic ray coupling sites (in the black hole vicinity versus at the large-scale jet-driven shock front), and jet precession parameters. Our findings indicate that when cosmic rays are injected near the black hole, they efficiently inhibit black hole accretion by suppressing the density before the jet propagates out to large radii. As a result, this leads to episodic black hole accretion, with the jet not having sufficient energy flux to reach large radii and impact cooling flows. Conversely, if the cosmic rays are injected at the jet-driven shock front, not only does the jet sustain a higher overall energy flux for an extended period, but it also disperses cosmic rays out to larger radii, more effectively suppressing the cooling flow. Furthermore, the period and angle of jet precession can influence the position of shock fronts. We identify an optimal range of jet precession periods ($\sim$ tens of Myr) that generates shocks at the inner circumgalactic medium, where cooling flows are most severe. We report that this specific configuration offers the most effective scenario for cosmic rays at the shock front to suppress the cooling flow and star formation.

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arXiv2024

AEOS: Star-by-Star Cosmological Simulations of Early Chemical Enrichment and Galaxy Formation

The AEOS project introduces a series of high-resolution cosmological simulations that model star-by-star chemical enrichment and galaxy formation in the early Universe, achieving 1 pc resolution. These simulations capture the complexities of galaxy evolution within the first ~300 Myr by modeling individual stars and their feedback processes. By incorporating chemical yields from individual stars, AEOS generates galaxies with diverse stellar chemical abundances, linking them to hierarchical galaxy formation and early nucleosynthetic events. These simulations underscore the importance of chemical abundance patterns in ancient stars as vital probes of early nucleosynthesis, star formation histories, and galaxy formation. We examine the metallicity floors of various elements resulting from Pop III enrichment, providing best-fit values for eight different metals (e.g., [O/H] = -4.0) to guide simulations without Pop III models. Additionally, we identify galaxies that begin star formation with Pop II after external enrichment and investigate the frequency of CEMP stars at varying metallicities. The AEOS simulations offer detailed insights into the relationship between star formation, feedback, and chemical enrichment. Future work will extend these simulations to later epochs to interpret the diverse stellar populations of the Milky Way and its satellites.

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arXiv2024

Learning the Universe: GalactISM simulations of resolved star formation and galactic outflows across main sequence and quenched galactic environments

We present a suite of six high-resolution chemo-dynamical simulations of isolated galaxies, spanning observed disk-dominated environments on the star-forming main sequence, as well as quenched, bulge-dominated environments. We compare and contrast the physics driving star formation and stellar feedback amongst the galaxies, with a view to modeling these processes in cosmological simulations. We find that the mass-loading of galactic outflows is coupled to the clustering of supernova explosions, which varies strongly with the rate of galactic rotation $Ω= v_c/R$ via the Toomre length, leading to smoother gas disks in the bulge-dominated galaxies. This sets an equation of state in the star-forming gas that also varies strongly with $Ω$, so that the bulge-dominated galaxies have higher mid-plane densities, lower velocity dispersions, and higher molecular gas fractions than their main sequence counterparts. The star formation rate in five out of six galaxies is independent of $Ω$, and is consistent with regulation by the mid-plane gas pressure alone. In the sixth galaxy, which has the most centrally-concentrated bulge and thus the highest $Ω$, we reproduce dynamical suppression of the star formation efficiency (SFE) in agreement with observations. This produces a transition away from pressure-regulated star formation.

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arXiv2024

Towards Implementation of the Pressure-Regulated, Feedback-Modulated Model of Star Formation in Cosmological Simulations: Methods and Application to TNG

Traditional star formation subgrid models implemented in cosmological galaxy formation simulations, such as that of Springel & Hernquist (2003, hereafter SH03), employ adjustable parameters to satisfy constraints measured in the local Universe. In recent years, however, theory and spatially-resolved simulations of the turbulent, multiphase, star-forming ISM have begun to produce new first-principles models, which when fully developed can replace traditional subgrid prescriptions. This approach has advantages of being physically motivated and predictive rather than empirically tuned, and allowing for varying environmental conditions rather than being tied to local Universe conditions. As a prototype of this new approach, by combining calibrations from the TIGRESS numerical framework with the Pressure-Regulated Feedback-Modulated (PRFM) theory, simple formulae can be obtained for both the gas depletion time and an effective equation of state. Considering galaxies in TNG50, we compare the "native" simulation outputs with post-processed predictions from PRFM. At TNG50 resolution, the total midplane pressure is nearly equal to the total ISM weight, indicating that galaxies in TNG50 are close to satisfying vertical equilibrium. The measured gas scale height is also close to theoretical equilibrium predictions. The slopes of the effective equations of states are similar, but with effective velocity dispersion normalization from SH03 slightly larger than that from current TIGRESS simulations. Because of this and the decrease in PRFM feedback yield at high pressure, the PRFM model predicts shorter gas depletion times than the SH03 model at high densities and redshift. Our results represent a first step towards implementing new, numerically calibrated subgrid algorithms in cosmological galaxy formation simulations.

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arXiv2024

CHARM: Creating Halos with Auto-Regressive Multi-stage networks

To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating particle-particle interactions (N-body simulations) are computationally expensive and prohibitive to scale to the large volumes and resolutions necessary for the upcoming datasets. Moreover, modeling the distribution of galaxies typically involves identifying virialized dark matter halos, which is also a time- and memory-consuming process for large N-body simulations, further exacerbating the computational cost. In this study, we introduce CHARM, a novel method for creating mock halo catalogs by matching the spatial, mass, and velocity statistics of halos directly from the large-scale distribution of the dark matter density field. We develop multi-stage neural spline flow-based networks to learn this mapping at redshift z=0.5 directly with computationally cheaper low-resolution particle mesh simulations instead of relying on the high-resolution N-body simulations. We show that the mock halo catalogs and painted galaxy catalogs have the same statistical properties as obtained from $N$-body simulations in both real space and redshift space. Finally, we use these mock catalogs for cosmological inference using redshift-space galaxy power spectrum, bispectrum, and wavelet-based statistics using simulation-based inference, performing the first inference with accelerated forward model simulations and finding unbiased cosmological constraints with well-calibrated posteriors. The code was developed as part of the Simons Collaboration on Learning the Universe and is publicly available at \url{https://github.com/shivampcosmo/CHARM}.

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arXiv2024

RIGEL: Simulating dwarf galaxies at solar mass resolution with radiative transfer and feedback from individual massive stars

We introduce the RIGEL model, a novel framework to self-consistently model the effects of stellar feedback in the multiphase ISM of dwarf galaxies with radiative transfer (RT) on a star-by-star basis. The RIGEL model integrates detailed implementations of feedback from individual massive stars into the RHD code, AREPO-RT. It forms individual massive stars from the resolved multiphase ISM by sampling the IMF and tracks their evolution individually. The lifetimes, photon production rates, mass-loss rates, and wind velocities of these stars are determined by their initial masses and metallicities based on a library that incorporates a variety of stellar models. The RT equations are solved in seven spectral bins accounting for the IR to HeII ionizing bands, using an M1 RT scheme. The thermochemistry model tracks the non-equilibrium H, He chemistry and the equilibrium abundance of CI, CII, OI, OII, and CO to capture the thermodynamics of all ISM phases. We evaluated the performance of the RIGEL model using $1\,{\rm M}_\odot$ resolution simulations of isolated dwarf galaxies. We found that the SFR and ISRF show strong positive correlations to the metallicity of the galaxy. Photoionization and photoheating can reduce the SFR by an order of magnitude by removing the available cold-dense gas fuel for star formation. The ISRF also changes the thermal structure of the ISM. Radiative feedback occurs immediately after the birth of massive stars and rapidly disperses the molecular clouds within 1 Myr. As a consequence, radiative feedback reduces the age spread of star clusters to less than 2 Myr, prohibits the formation of massive star clusters, and shapes the cluster initial mass function to a steep power-law form with a slope of $\sim-2$. The mass-loading factor of the fiducial galaxy has a median of $\sim50$, while turning off radiative feedback reduces this factor by an order of magnitude.

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arXiv2024

Arkenstone -- II. A model for unresolved cool clouds entrained in galactic winds in cosmological simulations

Arkenstone is a new scheme that allows multiphase, stellar feedback-driven winds to be included in coarse resolution cosmological simulations. The evolution of galactic winds and their subsequent impact on the circumgalactic medium are altered by exchanges of mass, energy, momentum, and metals between their component phases. These exchanges are governed by complex, small-scale physical processes that cannot be resolved in cosmological simulations. In this second presentation paper, we describe Arkenstone's novel cloud particle approach for modelling unresolvable cool clouds entrained in hot, fast winds. This general framework allows models of the cloud-wind interaction, derived from state-of-the-art high-resolution simulations, to be applied in a large-scale context. In this work, we adopt a cloud evolution model that captures simultaneous cloud mass loss to and gain from the ambient hot phase via turbulent mixing and radiative cooling, respectively. We demonstrate the scheme using non-cosmological idealized simulations of a galaxy with a realistic circumgalactic medium component, using the Arepo code. We show that the ability of a high-specific energy wind component to perform preventative feedback may be limited by heavy loading of cool clouds coupled into it. We demonstrate that the diverging evolution of clouds of initially differing masses leads to a complex velocity field for the cool phase and a cloud mass function that varies both spatially and temporally in a non-trivial manner. These latter two phenomena can manifest in the simulation because of our choice of a Lagrangian discretisation of the cloud population, in contrast to other proposed schemes. This is a Learning the Universe publication.

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arXiv2024

The All-Sky Impact of the LMC on the Milky Way Circumgalactic Medium

The first infall of the LMC into the Milky Way (MW) represents a large and recent disruption to the MW circumgalactic medium (CGM). In this work, we use idealized, hydrodynamical simulations of a MW-like CGM embedded in a live dark matter halo with an infalling LMC-like satellite initialized with its own CGM to understand how the encounter is shaping the global physical and kinematic properties of the MW CGM. First, we find that the LMC sources order-unity enhancements in MW CGM density, temperature, and pressure from a $\mathcal{M} \approx 2$ shock from the supersonic CGM-CGM collision, extending from the LMC to beyond $\sim R_{\rm 200, MW}$, enhancing column densities, X-ray brightness, the thermal Sunyaev-Zeldovich (tSZ) distortion, and potentially synchrotron emission from cosmic rays over large angular scales across the Southern Hemisphere. Second, the MW's reflex motion relative to its outer halo produces a dipole in CGM radial velocities, with $v_{\rm R} \pm 30-50$ km/s at $R > 50$ kpc in the Northern/Southern hemispheres respectively, consistent with measurements in the stellar halo. Finally, ram pressure strips most of the LMC CGM gas by the present day, leaving $\sim 10^{8-9} M_{\odot}$ of warm, ionized gas along the past orbit of the LMC moving at high radial and/or tangential velocities $\sim 50-100$ kpc from the MW. Massive satellites like the LMC leave their mark on the CGM structure of their host galaxies, and signatures from this interaction may manifest in key all-sky observables of the CGM of the MW and other massive galaxies.

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arXiv2024

Evolution of Star Cluster Within Galaxy using Self-consistent Hybrid Hydro/N-body Simulation

We introduce a GPU-accelerated hybrid hydro/N-body code (Enzo-N) designed to address the challenges of concurrently simulating star clusters and their parent galaxies. This task has been exceedingly challenging, primarily due to the considerable computational time required, which stems from the substantial scale difference between galaxies (~ 0.1 Mpc) and star clusters (~ pc). Yet, this significant scale separation means that particles within star clusters perceive those outside the star cluster in a semi-stationary state. By leveraging this aspect, we integrate the direct N-body code (Nbody6++GPU) into the cosmological (magneto-)hydrodynamic code (Enzo) through the utilization of the semi-stationary background acceleration approximation. We solve the dynamics of particles within star clusters using the direct N-body solver with regularization for few-body interactions, while evolving particles outside -- dark matter, gas, and stars -- using the particle-mesh gravity solver and hydrodynamic methods. We demonstrate that Enzo-N successfully simulates the co-evolution of star clusters and their parent galaxies, capturing phenomena such as core collapse of the star cluster and tidal stripping due to galactic tides. This comprehensive framework opens up new possibilities for studying the evolution of star clusters within galaxies, offering insights that were previously inaccessible.

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