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ABSTRACT Population III stars are possible precursors to early supermassive black holes (BHs). The presence of soft UV Lyman–Werner (LW) background radiation can suppress Population III star formation in minihaloes and allow them to form in pristine atomic-cooling haloes. In the absence of molecular hydrogen ($$\rm H_2$$) cooling, atomic-cooling haloes enable rapid collapse with suppressed fragmentation. High background LW fluxes from preceding star-formation have been proposed to dissociate $$\rm H_2$$. This flux can be supplemented by LW radiation from one or more Population III star(s) in the same halo, reducing the necessary background level. Here, we consider atomic-cooling haloes in which multiple protostellar cores form close to one another nearly simultaneously. We assess whether the first star’s LW radiation can dissociate nearby $$\rm H_2$$, enabling rapid accretion on to a nearby protostellar core, and the prompt formation of a second, supermassive star (SMS) from warm, atomically-cooled gas. We use a set of hydrodynamical simulations with the code enzo, with identical LW backgrounds centred on a halo with two adjacent collapsing gas clumps. When an additional large local LW flux is introduced, we observe immediate reductions in both the accretion rates and the stellar masses that form within these clumps. While the LW flux reduces the $$\text{H}_2$$ fraction and increases the gas temperature, the halo core’s potential well is too shallow to promptly heat the gas to $$\gtrsim$$1000 K and increase the second protostar’s accretion rate. We conclude that this internal LW feedback scenario is unlikely to facilitate SMS or massive BH seed formation. 

Abstract A key obstacle to accurate models of the first stars and galaxies is the vast range of distance scales that must be considered. While star formation occurs on sub-parsec scales within dark matter (DM) minihalos, it is influenced by large-scale baryon-dark matter streaming velocities (v_bc) and Lyman-Werner (LW) radiative feedback which vary significantly on scales of ∼100 Mpc. We present a novel approach to this issue in which we utilize artificial neural networks (NNs) to emulate the Population III (PopIII) and Population II (PopII) star formation histories of many small-scale cells given by a more complex semi-analytic framework based on DM halo merger trees. Within each simulation cell, the NN takes a set of input parameters that depend on the surrounding large-scale environment, such as the cosmic overdensity,δ(x⃗), andv_bcof the cell, then outputs the resulting star formation far more efficiently than is possible with the semi-analytic model. This rapid emulation allows us to self-consistently determine the LW background intensity on ∼100 Mpc scales, while simultaneously including the detailed merger histories (and corresponding star formation histories) of the low-mass minihalos that host the first stars. Comparing with the full semi-analytic framework utilizing DM halo merger trees, our NN emulators yield star formation histories with redshift-averaged errors of ∼7.3% and ∼5.2% for PopII and PopIII, respectively. When compared to a simpler sub-grid star formation prescription reliant on halo mass function integration, we find that the diversity of halo merger histories in our simulation leads to enhanced spatial fluctuations, an earlier transition from PopIII to PopII dominated star formation, and more scatter in star formation histories overall. 

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. 

Abstract We present a new self-consistent semianalytic model of the first stars and galaxies to explore the high-redshift (z≥ 15) Population III (PopIII) and metal-enriched star formation histories. Our model includes the detailed merger history of dark matter halos generated with Monte Carlo merger trees. We calibrate the minimum halo mass for PopIII star formation from recent hydrodynamical cosmological simulations that simultaneously include the baryon–dark matter streaming velocity, Lyman–Werner (LW) feedback, and molecular hydrogen self-shielding. We find an overall increase in the resulting star formation rate density (SFRD) compared to calibrations based on previous simulations (e.g., the PopIII SFRD is over an order of magnitude higher atz= 35−15). We evaluate the effect of the halo-to-halo scatter in this critical mass and find that it increases the PopIII stellar mass density by a factor ∼1.5 atz≥ 15. Additionally, we assess the impact of various semianalytic/analytic prescriptions for halo assembly and star formation previously adopted in the literature. For example, we find that models assuming smooth halo growth computed via abundance matching predict SFRDs similar to the merger tree model for our fiducial model parameters, but that they may underestimate the PopIII SFRD in cases of strong LW feedback. Finally, we simulate subvolumes of the Universe with our model both to quantify the reduction in total star formation in numerical simulations due to a lack of density fluctuations on spatial scales larger than the simulation box, and to determine spatial fluctuations in SFRD due to the diversity in halo abundances and merger histories. 

Abstract Fuzzy dark matter (FDM) is a proposed modification for the standard cold dark matter (CDM) model motivated by small-scale discrepancies in low-mass galaxies. Composed of ultralight (mass ∼ 10²²eV) axions with kiloparsec-scale de Broglie wavelengths, this is one of a class of candidates that predicts that the first collapsed objects form in relatively massive dark matter halos. This implies that the formation history of the first stars and galaxies would be very different, potentially placing strong constraints on such models. Here we numerically simulate the formation of the first stars in an FDM cosmology, following the collapse in a representative volume all the way down to primordial protostar formation including a primordial nonequilibrium chemical network and cooling for the first time. We find two novel results: first, the large-scale collapse results in a very thin and flat gas “pancake”; second, despite the very different cosmology, this pancake fragments until it forms protostellar objects indistinguishable from those in CDM. Combined, these results indicate that the first generation of stars in this model are also likely to be massive and, because of the sheet morphology, do not self-regulate, resulting in a massive Population III starburst. We estimate the total number of first stars forming in this extended structure to be 10⁴over 20 Myr using a simple model to account for the ionizing feedback from the stars, and should be observable with the James Webb Space Telescope. These predictions provide a potential smoking gun signature of FDM and similar dark matter candidates. 

Abstract We discover analytic equations that can infer the value of Ω_mfrom the positions and velocity moduli of halo and galaxy catalogs. The equations are derived by combining a tailored graph neural network (GNN) architecture with symbolic regression. We first train the GNN on dark matter halos from GadgetN-body simulations to perform field-level likelihood-free inference, and show that our model can infer Ω_mwith ∼6% accuracy from halo catalogs of thousands ofN-body simulations run with six different codes: Abacus, CUBEP³M, Gadget, Enzo, PKDGrav3, and Ramses. By applying symbolic regression to the different parts comprising the GNN, we derive equations that can predict Ω_mfrom halo catalogs of simulations run with all of the above codes with accuracies similar to those of the GNN. We show that, by tuning a single free parameter, our equations can also infer the value of Ω_mfrom galaxy catalogs of thousands of state-of-the-art hydrodynamic simulations of the CAMELS project, each with a different astrophysics model, run with five distinct codes that employ different subgrid physics: IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE. Furthermore, the equations also perform well when tested on galaxy catalogs from simulations covering a vast region in parameter space that samples variations in 5 cosmological and 23 astrophysical parameters. We speculate that the equations may reflect the existence of a fundamental physics relation between the phase-space distribution of generic tracers and Ω_m, one that is not affected by galaxy formation physics down to scales as small as 10h⁻¹kpc. 

Abstract We train graph neural networks on halo catalogs from Gadget N -body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogs contain ≲5000 halos with masses ≳10 10 h −1 M ⊙ in a periodic volume of ( 25 h − 1 Mpc ) 3 ; every halo in the catalog is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of Ω m and σ 8 with a mean relative error of ∼6%, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of Ω m and σ 8 when tested using halo catalogs from thousands of N -body simulations run with five different N -body codes: Abacus, CUBEP 3 M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer Ω m also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N -body codes are not converged on the relevant scales corresponding to these parameters. 

Abstract Catalytic membranes offer opportunities to develop modular, process‐intensified units. Dual‐functional materials, which integrate reactive and separation components in a single material, could play an important role in enabling them. Adapting the various characterization tools that are used to analyze the structures of metal‐based catalysts to these integrated structures could provide vital information for their design and implementation. In this perspective, we highlight the ways in which these tools can be used to analyze nonreactive membranes and non‐integrated systems where the catalyst and the membrane operate as two separate units. A methodology developed to analyze these comparatively simpler systems could be subsequently extended to integrated dual‐functional catalytic membranes. Thus, researchers from the catalysis and membranes communities can work together in a way that will not only lead to fundamental advancements in our understanding of catalytic membranes but also enable their transformation into real, scalable process‐intensified units. 

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.