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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., Comment: Submitted to MNRAS. 27 pages, 11 figures. This is a Learning the Universe publication

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., Comment: Submitted to ApJ. 22 pages, 15 Figures. Comments welcomed

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., Comment: 15 pages, 13 figures, Accepted for publication in ApJ

We present a new simulation suite for the star-forming interstellar medium (ISM) in galactic disks using the TIGRESS-NCR framework. Distinctive aspects of our simulation suite are: (1) sophisticated and comprehensive numerical treatments of essential physical processes including magnetohydrodynamics, self-gravity, and galactic differential rotation, as well as photochemistry, cooling, and heating coupled with ray-tracing UV radiation transfer and resolved supernova feedback and (2) wide parameter coverage including metallicity over $Z'\equiv Z/Z_\odot\sim0.1-3$, gas surface density $\Sigma_{\rm gas}\sim5-150 M_{\odot}{\rm pc^{-2}}$, and stellar surface density $\Sigma_{\rm star}\sim 1-50 M_{\odot}{\rm pc^{-2}}$. The range of emergent star formation rate surface density is $\Sigma_{\rm SFR}\sim 10^{-4}-0.5 M_{\odot}{\rm kpc^{-2}yr^{-1}}$ and ISM total midplane pressure is $P_{\rm tot}/k_B=10^3-10^6{\rm cm^{-3}K}$, with $P_{\rm tot}$ equal to the ISM weight $W$. For given $\Sigma_{\rm gas}$ and $\Sigma_{\rm star}$, we find $\Sigma_{\rm SFR} \propto Z'^{0.3}$. We provide an interpretation based on the pressure-regulated feedback-modulated (PRFM) star formation theory. We characterize feedback modulation in terms of the yield $\Upsilon$, defined as the ratio of each stress to $\Sigma_{\rm SFR}$. The thermal feedback yield varies sensitively with both weight and metallicity as $\Upsilon_{\rm th}\propto W^{-0.46}Z'^{-0.53}$, while the combined turbulent and magnetic feedback yield shows weaker dependence $\Upsilon_{\rm turb+mag}\propto W^{-0.22}Z'^{-0.18}$. The reduction in $\Sigma_{\rm SFR}$ at low metallicity is due mainly to enhanced thermal feedback yield, resulting from reduced attenuation of UV radiation. With the metallicity-dependent calibrations we provide, PRFM theory can be used for a new subgrid star formation prescription in cosmological simulations where the ISM is unresolved., Comment: Resubmitted to ApJ after minor revision

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., Comment: 27 pages, 18 figures; A&A in press; abstract slightly abridged; comments welcome

Bubbles driven by energy and mass injection from small scales are ubiquitous in astrophysical fluid systems and essential to feedback across multiple scales. In particular, O stars in young clusters produce high velocity winds that create hot bubbles in the surrounding gas. We demonstrate that the dynamical evolution of these bubbles is critically dependent upon the geometry of their interfaces with their surroundings and the nature of heat transport across these interfaces. These factors together determine the amount of energy that can be lost from the interior through cooling at the interface, which in turn determines the ability of the bubble to do work on its surroundings. We further demonstrate that the scales relevant to physical dissipation across this interface are extremely difficult to resolve in global numerical simulations of bubbles for parameter values of interest. This means the dissipation driving evolution of these bubbles in numerical simulations is often of a numerical nature. We describe the physical and numerical principles that determine the level of dissipation in these simulations; we use this, along with a fractal model for the geometry of the interfaces, to explain differences in convergence behavior between hydrodynamical and magneto-hydrodynamical simulations presented here. We additionally derive an expression for momentum as a function of bubble radius expected when the relevant dissipative scales are resolved and show that it still results in efficiently-cooled solutions as postulated in previous work., Comment: 41 pages, 20 figures, submitted to ApJ, comments welcome

The presence of dense, neutral hydrogen clouds in the hot, diffuse intra-group and intra-cluster medium is an important clue to the physical processes controlling the survival of cold gas and sheds light on cosmological baryon flows in massive halos. Advances in numerical modeling and observational surveys means that theory and observational comparisons are now possible. In this paper, we use the high-resolution TNG50 cosmological simulation to study the HI distribution in seven halos with masses similar to the Fornax galaxy cluster. Adopting observational sensitivities similar to the MeerKAT Fornax Survey (MFS), an ongoing HI survey that will probe to column densities of $10^{18}$ cm$^{-2}$, we find that Fornax-like TNG50 halos have an extended distribution of neutral hydrogen clouds. Within one virial radius, we predict the MFS will observe a total HI covering fraction around $\sim$ 12\% (mean value) for 10 kpc pixels and 6\% for 2 kpc pixels. If we restrict this to gas more than 10 half-mass radii from galaxies, the mean values only decrease mildly, to 10\% (4\%) for 10 (2) kpc pixels (albeit with significant halo-to-halo spread). Although there are large amounts of HI outside of galaxies, the gas seems to be associated with satellites, judging both by the visual inspection of projections and by comparison of the line of sight velocities of galaxies and intracluster HI., Comment: 20 pages, 13 figures, Accepted for publication in ApJ

The physical processes responsible for shaping how galaxies form and quench over time leave imprints on both the spatial (galaxy morphology) and temporal (star formation history; SFH) tracers that we use to study galaxies. While the morphology-SFR connection is well studied, the correlation with past star formation activity is not as well understood. To quantify this we present Katachi, an interpretable convolutional neural network (CNN) framework that learns the connection between the factors regulating star formation in galaxies on different spatial and temporal scales. Katachi is trained on 9904 galaxies at 0.02$<$z$<$0.1 in the SDSS-IV MaNGA DR17 sample to predict stellar mass (M$_*$; RMSE 0.22 dex), current star formation rate (SFR; RMSE 0.31 dex) and half-mass time (t$_{50}$; RMSE 0.23 dex). This information allows us to reconstruct non-parametric SFHs for each galaxy from \textit{gri} imaging alone. To quantify the morphological features informing the SFH predictions we use SHAP (SHapley Additive exPlanations). We recover the expected trends of M$_*$ governed by the growth of galaxy bulges, and SFR correlating with spiral arms and other star-forming regions. We also find the SHAP maps of D4000 are more complex than those of M$_*$ and SFR, and that morphology is correlated with t$_{50}$ even at fixed mass and SFR. Katachi serves as a scalable public framework to predict galaxy properties from large imaging surveys including Rubin, Roman, and Euclid, with large datasets of high SNR imaging across limited photometric bands., Comment: 35 pages, 25 figures. Accepted for publication in ApJ, comments are welcomed

The circumgalactic medium (CGM) of star-forming dwarf galaxies plays a key role in regulating the galactic baryonic cycle. We investigate how susceptible the CGM of dwarf satellite galaxies is to ram pressure stripping (RPS) in Milky Way-like environments. In a suite of hydrodynamical wind tunnel simulations, we model an intermediate-mass dwarf satellite galaxy ($M_{*} = 10^{7.2}~M_{\odot}$) with a multiphase interstellar medium (ISM; $M_{\rm ISM} = 10^{7.9}~M_{\odot}$) and CGM ($M_{\rm CGM,vir} = 10^{8.5}~M_{\odot}$) along two first-infall orbits to more than 500 Myr past pericenter of a Milky Way-like host. The spatial resolution is $\sim$79 pc in the star-forming ISM and $316-632$ pc in the CGM. Our simulations show that the dwarf satellite CGM removal is fast and effective: more than $95\%$ of the CGM mass is ram-pressure-stripped within a few hundred Myrs, even under a weak ram pressure orbit where the ISM stripping is negligible. The conditions for CGM survival are consistent with the analytical halo gas stripping predictions in McCarthy et al. (2008). We also find that including the satellite CGM does not effectively shield its galaxy, and therefore the ISM stripping rate is unaffected. Our results imply that a dwarf galaxy CGM is unlikely to be detected in satellite galaxies; and that the star formation of gaseous dwarf satellites is likely devoid of replenishment from a CGM., Comment: submitted to ApJ, comments welcome

Galaxy formation models within cosmological hydrodynamical simulations contain numerous parameters with non-trivial influences over the resulting properties of simulated cosmic structures and galaxy populations. It is computationally challenging to sample these high dimensional parameter spaces with simulations, particularly for halos in the high-mass end of the mass function. In this work, we develop a novel sampling and reduced variance regression method, CARPoolGP, which leverages built-in correlations between samples in different locations of high dimensional parameter spaces to provide an efficient way to explore parameter space and generate low variance emulations of summary statistics. We use this method to extend the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) to include a set of 768 zoom-in simulations of halos in the mass range of $10^{13} - 10^{14.5} M_\odot\,h^{-1}$ that span a 28-dimensional parameter space in the IllustrisTNG model. With these simulations and the CARPoolGP emulation method, we explore parameter trends in the Compton $Y-M$, black hole mass-halo mass, and metallicity-mass relations, as well as thermodynamic profiles and quenched fractions of satellite galaxies. We use these emulations to provide a physical picture of the complex interplay between supernova and active galactic nuclei feedback. We then use emulations of the $Y-M$ relation of massive halos to perform Fisher forecasts on astrophysical parameters for future Sunyaev-Zeldovich observations and find a significant improvement in forecasted constraints. We publicly release both the simulation suite and CARPoolGP software package., Comment: The manuscript was submitted to arxiv after receiving and responding to comments from the first referee report

This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schemata, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable and is designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry; inferring cosmology from matter power spectra and halo point clouds; characterizing progenitors in gravitational wave signals; capturing physical dust parameters from galaxy colors and luminosities; and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at https://github.com/maho3/ltu-ili., Comment: 22 pages, 10 figures, accepted in the Open Journal of Astrophysics. Code available at https://github.com/maho3/ltu-ili