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Abstract We explore the effect of variations in the Population III initial mass function (IMF) and star-by-star feedback on early galaxy formation and evolution using the Aeossimulations. We compare simulations with two different Population III IMFs:M_char = 10M_⊙and $M_{\max }=100M_{\odot }$(Aeos10) andM_char = 20M_⊙and $M_{\max }=300M_{\odot }$(Aeos20). Aeos20 produces significantly more ionizing photons, ionizing 30% of the simulation volume byz ≈ 14, compared to 9% in Aeos10. This enhanced ionization suppresses galaxy formation on the smallest scales. Differences in Population III IMF also affect chemical enrichment. Aeos20 produces Population II stars with higher abundances, relative to iron, of light andα-elements, a stronger odd–even effect, and a higher frequency of carbon-enhanced metal-poor stars. The abundance scatter between different Population II galaxies dominates the differences due to Population III IMF, though, implying a need for a larger sample of Population II stars to interpret the impact of Population III IMF on early chemical evolution. We also compare the Aeossimulations to traditional simulations that use single stellar population particles. We find that star-by-star modeling produces a steeper mass–metallicity relation due to less bursty feedback. These results highlight the strong influence of the Population III IMF on early galaxy formation and chemical evolution, emphasizing the need to account for IMF uncertainties in simulations and the importance of metal-poor Population II stellar chemical abundances when studying the first stars. 

Abstract Most galaxies, including the Milky Way, host a supermassive black hole (SMBH) at the center. These SMBHs can be observed out to high redshifts (z≥ 6) if the accretion rate is sufficiently large. However, we do not fully understand the mechanism through which these black holes form at early times. The heavy (or direct collapse) seeding mechanism has emerged as a probable contender in which the core of an atomic cooling halo directly collapses into a dense stellar cluster that could host supermassive stars that proceed to form a black hole seed of mass ∼ 10⁵M_⊙. We use the Renaissance Simulations to investigate the properties of 35 direct collapse black hole (DCBH) candidate host halos atz = 15–24 and compare them to noncandidate halos. We aim to understand what features differentiate halos capable of hosting a DCBH from the general halo population with the use of statistical analysis and machine learning methods. We examine 18 halo, central, and environmental properties. We find that DCBH candidacy is more dependent on a halo’s core internal properties than on exterior factors such as Lyman–Werner (LW) flux and distance to the closest galaxy; our analysis selects density and radial mass influx as the most important features (outside candidacy establishing features). Our results concur with the recent suggestion that DCBH host halos neither need to lie within a “Goldilocks zone” nor have a significant amount of LW flux to suppress cooling. This paper presents insight to the dynamics possibly occurring in potential DCBH host halos and seeks to provide guidance to DCBH subgrid formation models. 

Abstract We present a method that calibrates a semianalytic model to the Renaissance Simulations, a suite of cosmological hydrodynamical simulations with high-redshift galaxy formation. This approach combines the strengths of semianalytic techniques and hydrodynamical simulations, enabling the extension to larger volumes and lower redshifts that are inaccessible to simulations due to computational expense. Using a sample of Renaissance star formation histories from an average density region of the Universe, we construct a four-parameter prescription for metal-enriched star formation characterized by an initial bursty stage followed by a steady stage where stars are formed at constant efficiencies. Our model also includes a treatment of Pop III star formation where a minimum halo mass and log-normal distribution of stellar mass are adopted to match the numerical simulations. Star formation is generally well reproduced for halos with masses ≲10⁹M_⊙. Between 11 <z< 25 our model produces metal-enriched star formation rate densities (SFRDs) that typically agree with Renaissance within a factor of ∼2 for the average density region. Additionally, the total metal-enriched stellar mass only differs from Renaissance by about 10% atz∼ 11. For regions that are either more overdense or rarefied and not included in the calibration, we produce metal-enriched SFRDs that agree with Renaissance within a factor of ∼2 at high-zbut eventually differ by higher factors for later times. This is likely due to environmental dependencies not included in the model. Our star formation prescriptions can easily be adopted in other analytic or semianalytic works to match our calibration to Renaissance. 

The recent detections of a large number of candidate active galactic nuclei at high redshift (i.e.  $z\gtrsim 4$) has increased speculation that heavy seed massive black hole formation may be a required pathway. Here we re-implement the so-called Lyman-Werner (LW) channel model of Dijkstra et al. (2014) to calculate the expected number density of massive black holes formed through this channel. We further enhance this model by extracting information relevant to the model from the $\text{𝚁𝚎𝚗𝚊𝚒𝚜𝚜𝚊𝚗𝚌𝚎}$simulation suite. $\text{𝚁𝚎𝚗𝚊𝚒𝚜𝚜𝚊𝚗𝚌𝚎}$is a high-resolution suite of simulations ideally positioned to probe the high- $z$universe. Finally, we compare the LW-only channel against other models in the literature. We find that the LW-only channel results in a peak number density of massive black holes of approximately at $z\sim 10$. Given the growth requirements and the duty cycle of active galactic nuclei, this means that the LW-only is likely incompatible with recent JWST measurements and can, at most, be responsible for only a small subset of high- $z$active galactic nuclei. Other models from the literature (e.g. rapid assembly; relative velocities between baryons and dark matter) seem therefore better positioned, at present, to explain the high frequency of massive black holes at high $z$. 

Abstract 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 (SNe) of the first stars is enough to expel a majority of gas and injected metals beyond the virial radius of halos with massM_dm ≲ 10⁷M_⊙, regardless of the number of SNe. This prevents self-enrichment and results in a nonmonotonic increase in metallicity at early times. Most minihalos (M_dm ≳ 10⁵M_⊙) do not retain significant fractions of the yields produced within their virial radii until they have grown to halo masses ofM_dm ≳ 10⁷M_⊙. 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 redshiftz= 14.3), enrichment is dominated by core-collapse supernovae for all elements, but with a significant contribution from asymptotic giant branch winds to thes-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 Aeosin later epochs to trace the evolution of enrichment during the complete transition from Population III to Population II stars. 

Abstract The Aeosproject 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, Aeosgenerates 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 Population III enrichment, providing best-fit values for eight different metals (e.g., [O/H] = −4.0) to guide simulations without Population III models. Additionally, we identify galaxies that begin star formation with Population II after external enrichment and investigate the frequency of carbon-enhanced metal-poor stars at varying metallicities. The Aeossimulations 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. 

Theoretical models of galaxy formation and evolution are primarily investigated through cosmological simulations and semi-analytical models. The former method consumes O(10^6) core-hours explicitly modeling the dynamics of the galaxies, whereas the latter method only requires O(10^3) core-hours foregoing directly simulating internal structure for computational efficiency. In this work, we present a proof-of-concept machine learning regression model, using a graph neural network architecture, to predict the stellar mass of high-redshift galaxies solely from their dark matter merger trees, trained from a radiation hydrodynamics cosmological simulation of the first galaxies. 

Abstract Cosmic reionization is likely driven by UV starlight emanating from the first generations of galaxies. A galaxy’s UV escape fraction, or the fraction of photons escaping from the galaxy, is useful to quantify its contribution to reionization. However, the UV escape fraction is notoriously difficult to predict due to local environment dependency and variability over time. Using data from the Renaissance Simulations, we attempt to make predictions about the impact of the first stars and galaxies on their environments. We present a time-independent classification model using a general artificial neural network architecture to predict the UV escape fraction given other galaxy properties—namely halo mass, stellar mass, redshift, star formation rate, lookback time, and gas fraction. We find our validation accuracy to be approximately 50%–65%, depending on the data set size from each zoom-in region of the Renaissance Simulations.