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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 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. 

Abstract The Milky Way has accreted many ultra-faint dwarf galaxies (UFDs), and stars from these galaxies can be found throughout our Galaxy today. Studying these stars provides insight into galaxy formation and early chemical enrichment, but identifying them is difficult. Clustering stellar dynamics in 4D phase space (E,L_z,J_r,J_z) is one method of identifying accreted structure that is currently being utilized in the search for accreted UFDs. We produce 32 simulated stellar halos using particle tagging with the Caterpillar simulation suite and thoroughly test the abilities of different clustering algorithms to recover tidally disrupted UFD remnants. We perform over 10,000 clustering runs, testing seven clustering algorithms, roughly twenty hyperparameter choices per algorithm, and six different types of data sets each with up to 32 simulated samples. Of the seven algorithms, HDBSCAN most consistently balances UFD recovery rates and cluster realness rates. We find that, even in highly idealized cases, the vast majority of clusters found by clustering algorithms do not correspond to real accreted UFD remnants and we can generally only recover 6% of UFDs remnants at best. These results focus exclusively on groups of stars from UFDs, which have weak dynamic signatures compared to the background of other stars. The recoverable UFD remnants are those that accreted recently,z_accretion≲ 0.5. Based on these results, we make recommendations to help guide the search for dynamically linked clusters of UFD stars in observational data. We find that real clusters generally have higher median energy andJ_r, providing a way to help identify real versus fake clusters. We also recommend incorporating chemical tagging as a way to improve clustering results.