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Abstract 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 an MW-like CGM embedded in a 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 drives order-unity enhancements in MW CGM density, temperature, and pressure due to a $\mathcal{M}\approx 2$shock from the supersonic CGM–CGM collision. The resulting shock front extends from the LMC to beyond ∼R_200,MW, amplifying column densities, X-ray brightness, thermal Sunyaev–Zeldovich 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 induces a dipole in CGM radial velocities, withv_R ± 30–50 km s⁻¹atR > 50 kpc in the northern and southern hemispheres, respectively, consistent with measurements in the stellar halo. Finally, ram pressure strips most of the LMC’s CGM, leaving ∼10⁸⁻⁹M_⊙warm ionized gas along the past orbit of the LMC, moving at high radial and/or tangential velocities ∼50–100 kpc from the MW. Massive satellites like the LMC leave their mark on the CGM structure of their host galaxies, and signatures of such interactions may be observable in key all-sky tracers of the MW CGM and those of other massive galaxies. 

Abstract The majority of low-mass ( ${\log }_{10}{M}_{*}/M_{\odot }=9–10$) galaxies at high redshift (z > 1) appear elongated in projection. We use JWST-CEERS observations to explore the role of gravitational lensing in this puzzle. The typical galaxy–galaxy lensing shearγ ∼ 1% is too low to explain the predominance of elongated early galaxies with an ellipticitye ≈ 0.6. However, nonparametric quantile regression with Bayesian Additive Regression Trees (or BART) reveals hints of an excess of tangentially aligned source–lens pairs withγ > 10%. On larger scales, we also find evidence for weak-lensing shear. We rule out the null hypothesis of randomly oriented galaxies at ≳99% significance in multiple NIRCam chips, modules, and pointings. The number of such regions is small and attributable to chance, but coherent alignment patterns suggest otherwise. On the chip scale, the average complex ellipticity 〈e〉 ∼ 10% is nonnegligible and beyond the level of our point-spread function (PSF) uncertainties. The shear variance $〈{\overline{\gamma }}^2〉\sim 10^{-3}$is an order of magnitude above the conventional weak-lensing regime but is more sensitive to PSF systematics, intrinsic alignments, cosmic variance, and other biases. Taking it as an upper limit, the maximum implied “cosmic shear” is only a few percent and cannot explain the elongated shapes of early galaxies. The alignments themselves may arise from lensing by a protocluster or filament atz ∼ 0.75 where we find an overabundance of massive lens galaxies. We recommend a weak-lensing search for overdensities in “blank” deep fields with the James Webb Space Telescope and the Roman Space Telescope. 

Abstract The scaling of galaxy properties with halo mass suggests that feedback loops regulate star formation, but there is no consensus yet about how those feedback loops work. To help clarify discussions of galaxy-scale feedback, Paper I presented a very simple model for supernova feedback that it called the minimalist regulator model. This follow-up paper interprets that model and discusses its implications. The model itself is an accounting system that tracks all of the mass and energy associated with a halo’s circumgalactic baryons—the central galaxy’s atmosphere. Algebraic solutions for the equilibrium states of that model reveal that star formation in low-mass halos self-regulates primarily by expanding the atmospheres of those halos, ultimately resulting in stellar masses that are insensitive to the mass-loading properties of galactic winds. What matters most is the proportion of supernova energy that couples with circumgalactic gas. However, supernova feedback alone fails to expand galactic atmospheres in higher-mass halos. According to the minimalist regulator model, an atmospheric contraction crisis ensues, which may be what triggers strong black hole feedback. The model also predicts that circumgalactic medium properties emerging from cosmological simulations should depend largely on the specific energy of the outflows they produce, and we interpret the qualitative properties of several numerical simulations in light of that prediction. 

Abstract We present a suite of six high-resolution chemodynamical 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 among 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_circ/Rvia 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 midplane 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 midplane 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 in agreement with observations. This produces a transition away from pressure-regulated star formation. 

Abstract Traditional star formation subgrid models implemented in cosmological galaxy formation simulations, such as that of V. Springel & L. Hernquist (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 interstellar medium (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 postprocessed 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 toward implementing new, numerically calibrated subgrid algorithms in cosmological galaxy formation simulations. 

Abstract This paper presents a new framework for understanding the relationship between a galaxy and its circumgalactic medium (CGM). It focuses on howimbalancesbetween heating and cooling cause either expansion or contraction of the CGM. It does this by trackingallof the mass and energy associated with a halo’s baryons, including their gravitational potential energy, even if feedback has pushed some of those baryons beyond the halo’s virial radius. We show how a star-forming galaxy’s equilibrium state can be algebraically derived within the context of this framework, and we analyze how the equilibrium star formation rate depends on supernova feedback. We consider the consequences of varying the mass loading parameter ${\eta }_M\equiv {\dot{M}}_{\mathrm{wind}}/{\dot{M}}_{*}$relating a galaxy’s gas mass outflow rate ( ${\dot{M}}_{\mathrm{wind}}$) to its star formation rate ( ${\dot{M}}_{*}$) and obtain results that challenge common assumptions. In particular, we find that equilibrium star formation rates in low-mass galaxies are generally insensitive to mass loading, and when mass loading does matter, increasing it actually results inmorestar formation because more supernova energy is needed to resist atmospheric contraction. 

Abstract Galaxy formation models within cosmological hydrodynamical simulations contain numerous parameters with nontrivial 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, in particular 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 to include a set of 768 zoom-in simulations of halos in the mass range of 10¹³–10^14.5M_⊙h⁻¹that span a 28-dimensional parameter space in the IllustrisTNG model. With these simulations and the CARPoolGP emulation method, we explore parameter trends in the ComptonY–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 theY–Mrelation 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. 

Abstract The 3D geometries of high-redshift galaxies remain poorly understood. We build a differentiable Bayesian model and use Hamiltonian Monte Carlo to efficiently and robustly infer the 3D shapes of star-forming galaxies in James Webb Space Telescope Cosmic Evolution Early Release Science observations with $\log M_{*}/M_{\odot }=9.0–10.5$atz= 0.5–8.0. We reproduce previous results from the Hubble Space Telescope Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey in a fraction of the computing time and constrain the mean ellipticity, triaxiality, size, and covariances with samples as small as ∼50 galaxies. We find high 3D ellipticities for all mass–redshift bins, suggesting oblate (disky) or prolate (elongated) geometries. We break that degeneracy by constraining the mean triaxiality to be ∼1 for $\log M_{*}/M_{\odot }=9.0–9.5$dwarfs atz> 1 (favoring the prolate scenario), with significantly lower triaxialities for higher masses and lower redshifts indicating the emergence of disks. The prolate population traces out a “banana” in the projected $b/a–\log a$diagram with an excess of low-b/a, large- $\log a$galaxies. The dwarf prolate fraction rises from ∼25% atz= 0.5–1.0 to ∼50%–80% atz= 3–8. Our results imply a second kind of disk settling from oval (triaxial) to more circular (axisymmetric) shapes with time. We simultaneously constrain the 3D size–mass relation and its dependence on 3D geometry. High-probability prolate and oblate candidates show remarkably similar Sérsic indices (n∼ 1), nonparametric morphological properties, and specific star formation rates. Both tend to be visually classified as disks or irregular, but edge-on oblate candidates show more dust attenuation. We discuss selection effects, follow-up prospects, and theoretical implications. 

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 schema, 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, 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; characterising 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.