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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.

 

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 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 Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages. 

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.