Search for: All records

Abstract The observed rest-UV luminosity function at cosmic dawn (z∼ 8–14) measured by JWST revealed an excess of UV-luminous galaxies relative to many prelaunch theoretical predictions. A high star formation efficiency (SFE) and a top-heavy initial mass function (IMF) are among the mechanisms proposed for explaining this excess. Although a top-heavy IMF has been proposed for its ability to increase the light-to-mass ratio (Ψ_UV), the resulting enhanced radiative pressure from young stars could decrease the SFE, potentially driving galaxy luminosities back down. In this Letter, we use idealized radiation hydrodynamic simulations of star cluster formation to explore the effects of a top-heavy IMF on the SFE of clouds typical of the high-pressure conditions found at these redshifts. We find that the SFE in star clusters with solar-neighborhood-like dust abundance decreases with increasingly top-heavy IMFs—by ∼20% for an increase of a factor of 4 in Ψ_UVand by 50% for a factor of ∼10 in Ψ_UV. However, we find that an expected decrease in the dust-to-gas ratio (∼0.01 × solar) at these redshifts can completely compensate for the enhanced light output. This leads to a (cloud-scale; ∼10 pc) SFE that is ≳70% even for a factor of 10 increase in Ψ_UV, implying that highly efficient star formation is unavoidable for high surface density and low-metallicity conditions. Our results suggest that a top-heavy IMF, if present, likely coexists with efficient star formation in these galaxies. 

Abstract We present a new suite of numerical simulations of 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 direct ray-tracing UV radiation transfer and resolved supernova feedback and (2) wide parameter coverage including the variation in metallicity over $Z\prime \equiv Z/Z_{\odot }\sim 0.1-3$, gas surface density Σ_gas∼ 5–150M_⊙pc⁻², and stellar surface density Σ_star∼ 1–50M_⊙pc⁻². The range of emergent star formation rate surface density is Σ_SFR∼ 10⁻⁴–0.5M_⊙kpc⁻²yr⁻¹, and ISM total midplane pressure isP_tot/k_B= 10³–10⁶cm⁻³K, withP_totequal to the ISM weight $\mathcal{W}$. For given Σ_gasand Σ_star, we find ${\mathrm{\Sigma }}_{\mathrm{SFR}}\propto Z{\prime }^{0.3}$. We provide an interpretation based on the pressure-regulated feedback-modulated (PRFM) star formation theory. The total midplane pressure consists of thermal, turbulent, and magnetic stresses. We characterize feedback modulation in terms of the yield ϒ, defined as the ratio of each stress to Σ_SFR. The thermal feedback yield varies sensitively with both weight and metallicity as ${\mathrm{\Upsilon }}_{\mathrm{th}}\propto {\mathcal{W}}^{-0.46}Z{\prime }^{-0.53}$, while the combined turbulent and magnetic feedback yield shows weaker dependence ${\mathrm{\Upsilon }}_{\mathrm{turb}+\mathrm{mag}}\propto {\mathcal{W}}^{-0.22}Z{\prime }^{-0.18}$. The reduction in Σ_SFRat 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. 

Abstract We make an in-depth analysis of different active galactic nuclei (AGN) jet models’ signatures, inducing quiescence in galaxies with a halo mass of 10¹²M_⊙. Three jet models, including cosmic-ray-dominant, hot thermal, and precessing kinetic jets, are studied at two energy flux levels each, compared to a jet-free, stellar feedback-only simulation. Each of our simulations is idealized isolated galaxy simulations with AGN jet powers that are constant in time and generated using GIZMO and with FIRE stellar feedback. We examine the distribution of Mgii, Ovi, and Oviiiions, alongside gas temperature and density profiles. Low-energy ions, like Mgii, concentrate in the interstellar medium (ISM), while higher energy ions, e.g., Oviii, prevail at the AGN jet cocoon’s edge. High-energy flux jets display an isotropic ion distribution with lower overall density. High-energy thermal or cosmic-ray jets pressurize at smaller radii, significantly suppressing core density. The cosmic-ray jet provides extra pressure support, extending cool and warm gas distribution. A break in the ion-to-mass ratio slope in Oviand Oviiiis demonstrated in the ISM-to-circumgalactic medium (CGM) transition (between 10 and 30 kpc), growing smoothly toward the CGM at greater distances. 

ABSTRACT The physical origin of the seeds of supermassive black holes (SMBHs), with postulated initial masses ranging from ∼105 M⊙ to as low as ∼102 M⊙, is currently unknown. Most existing cosmological hydrodynamic simulations adopt very simple, ad hoc prescriptions for BH seeding and seed at unphysically high masses ∼105–106 M⊙. In this work, we introduce a novel sub-grid BH seeding model for cosmological simulations that is directly calibrated to high-resolution zoom simulations that explicitly resolve ∼103 M⊙ seeds forming within haloes with pristine, dense gas. We trace the BH growth along galaxy merger trees until their descendants reach masses of ∼104 or 105 M⊙. The results are used to build a new stochastic seeding model that directly seeds these descendants in lower resolution versions of our zoom region. Remarkably, we find that by seeding the descendants simply based on total galaxy mass, redshift and an environmental richness parameter, we can reproduce the results of the detailed gas-based seeding model. The baryonic properties of the host galaxies are well reproduced by the mass-based seeding criterion. The redshift-dependence of the mass-based criterion captures the combined influence of halo growth, dense gas formation, and metal enrichment on the formation of ∼103 M⊙ seeds. The environment-based seeding criterion seeds the descendants in rich environments with higher numbers of neighbouring galaxies. This accounts for the impact of unresolved merger dominated growth of BHs, which produces faster growth of descendants in richer environments with more extensive BH merger history. Our new seed model will be useful for representing a variety of low-mass seeding channels within next-generation larger volume uniform cosmological simulations. 

ABSTRACT ‘Runaway stars’ might play a role in driving galactic outflows and enriching the circumgalactic medium with metals. To study this effect, we carry out high-resolution dwarf galaxy simulations that include velocity ‘kicks’ to massive stars above eigth solar masses. We consider two scenarios, one that adopts a power law velocity distribution for kick velocities, resulting in more stars with high-velocity kicks, and a more moderate scenario with a Maxwellian velocity distribution. We explicitly resolve the multiphase interstellar medium (ISM) and include non-equilibrium cooling and chemistry. We sample individual massive stars from an IMF and follow their radiation input and SN feedback (core-collapse) channel at the end of their lifetime. In the simulations with runaway stars, we add additional (natal) velocity kicks that mimic two- and three-body interactions that cannot be fully resolved in our simulations. We find that including runaway or ‘walkaway’ star scenarios impacts mass, metal, momentum, and energy outflows as well as the corresponding loading factors. The effect on the mass loading factor is small, but we find an increase in the metal loading by a factor of 1.5 to 2. The momentum loading increases by a factor of 1.5–2. The energy loading increases by roughly a factor of 5 when runaway stars are included. Additionally, we find that the overall level of star formation is increased in the models that include runaway stars. We conclude that the inclusion of runaway stars could have an impact on the global star formation and subsequent outflow properties of dwarf galaxies. 

Abstract The circumgalactic medium (CGM) plays a pivotal role in regulating gas flows around galaxies and thus shapes their evolution. However, the details of how galaxies and their CGM coevolve remain poorly understood. We present a new time-dependent two-zone model that self-consistently tracks not just mass and metal flows between galaxies and their CGM but also the evolution of the global thermal and turbulent kinetic energy of the CGM. Our model accounts for heating and turbulence driven by both supernova winds and cosmic accretion as well as radiative cooling, turbulence dissipation, and halo outflows due to CGM overpressurization. We demonstrate that, depending on parameters, the CGM can undergo a phase transition (“thermalization”) from a cool, turbulence-supported phase to a virial-temperature, thermally supported phase. This CGM phase transition is largely determined by the ability of radiative cooling to balance heating from supernova winds and turbulence dissipation. We perform an initial calibration of our model to the FIRE-2 cosmological hydrodynamical simulations and show that it can approximately reproduce the baryon cycles of the simulated halos. In particular, we find that, for these parameters, the phase transition occurs at high redshift in ultrafaint progenitors and at low redshift in classicalM_vir∼ 10¹¹M_⊙dwarfs, while Milky Way–mass halos undergo the transition atz≈ 0.5. We see a similar transition in the simulations though it is more gradual, likely reflecting radial dependence and multiphase gas not captured by our model. We discuss these and other limitations of the model and possible future extensions. 

Abstract Galactic outflows driven by supernovae (SNe) are thought to be a powerful regulator of a galaxy’s star-forming efficiency. Mass, energy, and metal outflows (η_M,η_E, andη_Z, here normalized by the star formation rate, the SNe energy, and metal production rates, respectively) shape galaxy properties by both ejecting gas and metals out of the galaxy and by heating the circumgalactic medium (CGM), preventing future accretion. Traditionally, models have assumed that galaxies self-regulate by ejecting a large fraction of the gas, which enters the interstellar medium (ISM), although whether such high mass loadings agree with observations is still unclear. To better understand how the relative importance of ejective (i.e., high mass loading) versus preventative (i.e., high energy loading) feedback affects the present-day properties of galaxies, we develop a simple gas-regulator model of galaxy evolution, where the stellar mass, ISM, and CGM are modeled as distinct reservoirs which exchange mass, metals, and energy at different rates within a growing halo. Focusing on the halo mass range from 10¹⁰to 10¹²M_⊙, we demonstrate that, with reasonable parameter choices, we can reproduce the stellar-to-halo mass relation and the ISM-to-stellar mass relation with low-mass-loaded (η_M∼ 0.1–10) but high-energy-loaded (η_E∼ 0.1–1) winds, with self-regulation occurring primarily through heating and cooling of the CGM. We show that the model predictions are robust against changes to the mass loading of outflows but are quite sensitive to our choice of the energy loading, preferringη_E∼ 1 for the lowest-mass halos and ∼0.1 for Milky Way–like halos. 

Abstract We present a suite of high-resolution simulations of an isolated dwarf galaxy using four different hydrodynamical codes: Gizmo , Arepo , Gadget , and Ramses . All codes adopt the same physical model, which includes radiative cooling, photoelectric heating, star formation, and supernova (SN) feedback. Individual SN explosions are directly resolved without resorting to subgrid models, eliminating one of the major uncertainties in cosmological simulations. We find reasonable agreement on the time-averaged star formation rates as well as the joint density–temperature distributions between all codes. However, the Lagrangian codes show significantly burstier star formation, larger SN-driven bubbles, and stronger galactic outflows compared to the Eulerian code. This is caused by the behavior in the dense, collapsing gas clouds when the Jeans length becomes unresolved: Gas in Lagrangian codes collapses to much higher densities than that in Eulerian codes, as the latter is stabilized by the minimal cell size. Therefore, more of the gas cloud is converted to stars and SNe are much more clustered in the Lagrangian models, amplifying their dynamical impact. The differences between Lagrangian and Eulerian codes can be reduced by adopting a higher star formation efficiency in Eulerian codes, which significantly enhances SN clustering in the latter. Adopting a zero SN delay time reduces burstiness in all codes, resulting in vanishing outflows as SN clustering is suppressed. 

Abstract As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine-learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but they must be trained carefully on large and representative data sets. We present a new “hump” of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter-only simulations of (100h⁻¹cMpc)³with different cosmological parameters (Ω_mandσ₈) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof of concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation, count-in-cells, and void probability functions, and we probe nonlinear and linear scales across 0.68 <R<27h⁻¹cMpc. We find our neural networks can both marginalize over the uncertainties in astrophysics to constrain cosmology to 3%–8% error across various types of galaxy selections, while simultaneously learning about the SC-SAM astrophysical parameters. This work encompasses vital first steps toward creating algorithms able to marginalize over the uncertainties in our galaxy formation models and measure the underlying cosmology of our Universe. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and it offers great potential to many applications of machine learning in astrophysics:https://camels-sam.readthedocs.io. 

ABSTRACT The early growth of black holes (BHs) in high-redshift galaxies is likely feedback regulated. While radiative feedback has been extensively studied, the role of mechanical feedback has received less scrutiny to date. Here, we use high-resolution parsec-scale hydrodynamical simulations to study jet propagation and its effect on 100 M⊙ BH accretion in the dense, low-metallicity gas expected in early protogalaxies. As the jet propagates, it shocks the surrounding gas forming a jet cocoon. The cocoon consists of a rapidly cooling cold phase at the interface with the background gas and an overpressured subsonic phase of reverse shock-heated gas filling the interior. We vary the background gas density and temperature, BH feedback efficiency, and the jet model. We found that the width of the jet cocoon roughly follows a scaling derived by assuming momentum conservation in the jet-propagation direction and energy conservation in the lateral directions. Depending on the assumed gas and jet properties, the cocoon either stays elongated to large radii or isotropizes before reaching the Bondi radius, forming a nearly spherical bubble. Lower jet velocities and higher background gas densities result in self-regulation to higher momentum fluxes and elongated cocoons. In all cases, the outward cocoon momentum flux balances the inward inflowing gas momentum flux near the Bondi radius, which ultimately regulates BH accretion. The time-averaged accretion rate always remains below the Bondi rate, and exceeds the Eddington rate only if the ambient medium is dense and cold, and/or the jet is weak (low velocity and mass loading).