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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.
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This content will become publicly available on February 1, 2026
From dark matter minihalos to large-scale radiative feedback: a self-consistent 3D simulation of the first stars and galaxies using neural networks
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 (vbc) 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), andvbcof 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.
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- Award ID(s):
- 2009309
- PAR ID:
- 10627199
- Publisher / Repository:
- IOP Publishing Ltd
- Date Published:
- Journal Name:
- Journal of Cosmology and Astroparticle Physics
- Volume:
- 2025
- Issue:
- 02
- ISSN:
- 1475-7516
- Page Range / eLocation ID:
- 043
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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