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Abstract Simulations of galaxy formation are mostly unable to resolve the energy-conserving phase of individual supernova events, having to resort to subgrid models to distribute the energy and momentum resulting from stellar feedback. However, the properties of these simulated galaxies, including the morphology, stellar mass formed, and the burstiness of the star formation history, are highly sensitive to the numerical choices adopted in these subgrid models. Using the SMUGGLE stellar feedback model, we carry out idealized simulations of anM_vir∼ 10¹⁰M_⊙dwarf galaxy, a regime where most simulation codes predict significant burstiness in star formation, resulting in strong gas flows that lead to the formation of dark matter cores. We find that by varying only the directional distribution of momentum imparted from supernovae to the surrounding gas, while holding the total momentum per supernova constant, bursty star formation may be amplified or completely suppressed, and the total stellar mass formed can vary by as much as a factor of ∼3. In particular, when momentum is primarily directed perpendicular to the gas disk, less bursty and lower overall star formation rates result, yielding less gas turbulence, more disky morphologies, and a retention of cuspy dark matter density profiles. An improved understanding of the nonlinear coupling of stellar feedback into inhomogeneous gaseous media is thus needed to make robust predictions for stellar morphologies and dark matter core formation in dwarfs independent of uncertain numerical choices in the baryonic treatment. 

Abstract We introduce the DaRk mattEr and Astrophysics with Machine learning and Simulations (DREAMS) project, an innovative approach to understanding the astrophysical implications of alternative dark matter (DM) models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over DM physics, astrophysics, and cosmology in modeling a range of systems—from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of warm dark matter (WDM), each comprising 1024 simulations generated using thearepocode. One suite consists of uniform-box simulations covering a ${\left(25h^{-1}\mathrm{Mpc}\right)}^3$volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the WDM particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and convolutional neural networks, to demonstrate how such simulation suites can be used to disentangle the effects of DM and baryonic physics on galactic properties. The DREAMS project can be extended further to include different DM models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of DM on these scales. 

ABSTRACT JWST has revealed a large population of accreting black holes (BHs) in the early Universe. Recent work has shown that even after accounting for possible systematic biases, the high-z$$M_*{\!-\!}M_{\rm \rm bh}$$ relation can be above the local scaling relation by $$\gt 3\sigma$$. To understand the implications of these overmassive high-z BHs, we study the BH growth at $$z\sim 4{\!-\!}7$$ using the $$[18~\mathrm{Mpc}]^3$$BRAHMA cosmological simulations with systematic variations of heavy seed models that emulate direct collapse black hole (DCBH) formation. In our least restrictive seed model, we place $$\sim 10^5~{\rm M}_{\odot }$$ seeds in haloes with sufficient dense and metal-poor gas. To model conditions for direct collapse, we impose additional criteria based on a minimum Lyman Werner flux (LW flux $$=10~J_{21}$$), maximum gas spin, and an environmental richness criterion. The high-z BH growth in our simulations is merger dominated, with a relatively small contribution from gas accretion. The simulation that includes all the above seeding criteria fails to reproduce an overmassive high-z$$M_*{\!-\!}M_{\rm bh}$$ relation consistent with observations (by factor of $$\sim 10$$ at $$z\sim 4$$). However, more optimistic models that exclude the spin and environment based criteria are able to reproduce the observed relations if we assume $$\lesssim 750~\mathrm{Myr}$$ delay times between host galaxy mergers and subsequent BH mergers. Overall, our results suggest that current JWST observations may be explained with heavy seeding channels if their formation is more efficient than currently assumed DCBH conditions. Alternatively, we may need higher initial seed masses, additional contributions from lighter seeds to BH mergers, and / or more efficient modes for BH accretion. 

ABSTRACT While the first “seeds” of supermassive black holes (BH) can range from $$\sim 10^2-10^6 \rm ~{\rm M}_{\odot }$$, the lowest mass seeds ($$\lesssim 10^3~\rm {\rm M}_{\odot }$$) are inaccessible to most cosmological simulations due to resolution limitations. We present our new BRAHMA simulations that use a novel flexible seeding approach to predict the $$z\ge 7$$ BH populations for low-mass seeds. We ran two types of boxes that model $$\sim 10^3~\rm {\rm M}_{\odot }$$ seeds using two distinct but mutually consistent seeding prescriptions at different simulation resolutions. First, we have the highest resolution $$[9~\mathrm{Mpc}]^3$$ (BRAHMA-9-D3) boxes that directly resolve $$\sim 10^3~\rm {\rm M}_{\odot }$$ seeds and place them within haloes with dense, metal-poor gas. Second, we have lower resolution, larger volume $$[18~\mathrm{Mpc}]^3$$ (BRAHMA-18-E4), and $$\sim [36~\mathrm{Mpc}]^3$$ (BRAHMA-36-E5) boxes that seed their smallest resolvable $$\sim 10^4~\&~10^5~\mathrm{{\rm M}_{\odot }}$$ BH descendants using new stochastic seeding prescriptions calibrated using BRAHMA-9-D3. The three boxes together probe key BH observables between $$\sim 10^3\,\mathrm{ and}\,10^7~\rm {\rm M}_{\odot }$$. The active galactic nuclei (AGN) luminosity function variations are small (factors of $$\sim 2-3$$) at the anticipated detection limits of potential future X-ray facilities ($$\sim 10^{43}~ \mathrm{ergs~s^{-1}}$$ at $$z\sim 7$$). Our simulations predict BHs $$\sim 10-100$$ times heavier than the local $$M_*$$ versus $$M_{\mathrm{ bh}}$$ relations, consistent with several JWST-detected AGN. For different seed models, our simulations merge binaries at $$\sim 1-15~\mathrm{kpc}$$, with rates of $$\sim 200-2000$$ yr−1 for $$\gtrsim 10^3~\rm {\rm M}_{\odot }$$ BHs, $$\sim 6-60$$ yr−1 for $$\gtrsim 10^4~\rm {\rm M}_{\odot }$$ BHs, and up to $$\sim 10$$ yr−1 amongst $$\gtrsim 10^5~\rm {\rm M}_{\odot }$$ BHs. These results suggest that Laser Interferometer Space Antenna mission has promising prospects for constraining seed models. 

ABSTRACT Stellar feedback plays a crucial role in regulating baryon cycles of a galactic ecosystem, and may manifest itself in the formation of superbubbles in the interstellar medium. In this work, we used a set of high-resolution simulations to systematically study the properties and evolution of superbubbles in galactic environments. The simulations were based on the SMUGGLE galaxy formation framework using the hydrodynamical moving-mesh code arepo, reaching a spatial resolution of $$\sim 4 \, \rm pc$$ and mass resolution of $$\sim 10^3 \, \rm M_{\odot }$$. We identified superbubbles and tracked their time evolution using the parent stellar associations within the bubbles. The X-ray luminosity-size distribution of superbubbles in the fiducial run is largely consistent with the observations of nearby galaxies. The size of superbubbles shows a double-peaked distribution, with the peaks attributed to early feedback (radiative and stellar wind feedback) and supernova feedback. The early feedback tends to suppress the subsequent supernova feedback, and it is strongly influenced by star formation efficiency, which regulates the environmental density. Our results show that the volume filling factor of hot gas (T > 105.5 K) is about $$12~{{\ \rm per\ cent}}$$ averaged over a region of 4 kpc in height and 20 kpc in radius centred on the disc of the galaxy. Overall, the properties of superbubbles are sensitive to the choice of subgrid galaxy formation models and can, therefore, be used to constrain these models. 

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 The scatter about the mass-metallicity relation (MZR) has a correlation with the star formation rate (SFR) of galaxies. The lack of evidence of evolution in correlated scatter at z ≲ 2.5 leads many to refer to the relationship between mass, metallicity, and SFR as the Fundamental Metallicity Relation (FMR). Yet, recent high-redshift (z > 3) JWST observations have challenged the fundamental (i.e. redshift-invariant) nature of the FMR. In this work, we show that the cosmological simulations Illustris, IllustrisTNG, and Evolution and Assembly of GaLaxies and their Environment (EAGLE) all predict MZRs that exhibit scatter with a secondary dependence on SFR up to z = 8. We introduce the concept of a ‘strong’ FMR, where the strength of correlated scatter does not evolve with time, and a ‘weak’ FMR, where there is some time evolution. We find that each simulation analysed has a statistically significant weak FMR – there is non-negligible evolution in the strength of the correlation with SFR. Furthermore, we show that the scatter is reduced an additional ∼10–40 per cent at z ≳ 3 when using a weak FMR, compared to assuming a strong FMR. These results highlight the importance of avoiding coarse redshift binning when assessing the FMR. 

ABSTRACT The metal content of galaxies provides a window into their formation in the full context of the cosmic baryon cycle. In this study, we examine the relationship between stellar mass and stellar metallicity (MZ*R) in the hydrodynamic simulations Illustris, TNG, and EAGLE (Evolution and Assembly of GaLaxies and their Environment) to understand the global properties of stellar metallicities within the feedback paradigm employed by these simulations. Interestingly, we observe significant variations in the overall normalization and redshift evolution of the MZ*R across the three simulations. However, all simulations consistently demonstrate a tertiary dependence on the specific star formation rate (sSFR) of galaxies. This finding parallels the relationship seen in both simulations and observations between stellar mass, gas-phase metallicity, and some proxy of galaxy gas content (e.g. SFR, gas fraction, and atomic gas mass). Since we find this correlation exists in all three simulations, each employing a subgrid treatment of the dense, star-forming interstellar medium (ISM) to simulate smooth stellar feedback, we interpret this result as a fairly general feature of simulations of this kind. Furthermore, with a toy analytic model, we propose that the tertiary correlation in the stellar component is sensitive to the extent of the ‘burstiness’ of feedback within galaxies. 

Abstract We present deep Hubble Space Telescope photometry of 10 targets from Treasury Program GO-14734, including six confirmed ultrafaint dwarf (UFD) galaxies, three UFD candidates, and one likely globular cluster. Six of these targets are satellites of, or have interacted with, the Large Magellanic Cloud (LMC). We determine their structural parameters using a maximum-likelihood technique. Using our newly derived half-light radius (r_h) andV-band magnitude (M_V) values in addition to literature values for other UFDs, we find that UFDs associated with the LMC do not show any systematic differences from Milky Way UFDs in the magnitude–size plane. Additionally, we convert simulated UFD properties from the literature into theM_V–r_hobservational space to examine the abilities of current dark matter (DM) and baryonic simulations to reproduce observed UFDs. Some of these simulations adopt alternative DM models, thus allowing us to also explore whether theM_V–r_hplane could be used to constrain the nature of DM. We find no differences in the magnitude–size plane between UFDs simulated with cold, warm, and self-interacting DM, but note that the sample of UFDs simulated with alternative DM models is quite limited at present. As more deep, wide-field survey data become available, we will have further opportunities to discover and characterize these ultrafaint stellar systems and the greater low surface-brightness universe. 

ABSTRACT We present a new suite of over 1500 cosmological N-body simulations with varied warm dark matter (WDM) models ranging from 2.5 to 30 keV. We use these simulations to train Convolutional Neural Networks (CNNs) to infer WDM particle masses from images of DM field data. Our fiducial setup can make accurate predictions of the WDM particle mass up to 7.5 keV with an uncertainty of ±0.5 keV at a 95 per cent confidence level from (25 h−1Mpc)2 maps. We vary the image resolution, simulation resolution, redshift, and cosmology of our fiducial setup to better understand how our model is making predictions. Using these variations, we find that our models are most dependent on simulation resolution, minimally dependent on image resolution, not systematically dependent on redshift, and robust to varied cosmologies. We also find that an important feature to distinguish between WDM models is present with a linear size between 100 and 200 h−1 kpc. We compare our fiducial model to one trained on the power spectrum alone and find that our field-level model can make two times more precise predictions and can make accurate predictions to two times as massive WDM particle masses when used on the same data. Overall, we find that the field-level data can be used to accurately differentiate between WDM models and contain more information than is captured by the power spectrum. This technique can be extended to more complex DM models and opens up new opportunities to explore alternative DM models in a cosmological environment.