Abstract Theoretical models of galaxy formation and evolution are primarily investigated through cosmological simulations and semi-analytical models. The former method consumes core-hours explicitly modeling the dynamics of the galaxies, whereas the latter method only requires 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.
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Early Bright Galaxies from Helium Enhancements in High-Redshift Star Clusters
The first few cycles of JWST have identified an overabundance of UV-bright galaxies and a general excess of UV luminosity density at compared to expectations from most (pre-JWST) theoretical models. Moreover, some of the brightest high-redshift spectroscopically confirmed galaxies exhibit peculiar chemical abundance patterns, most notably extremely high N/O ratios. Since N/O has been empirically shown to scale strongly with He/H, as expected for hot hydrogen burning, these same bright high-redshift galaxies are likely also helium-enhanced. Under simplistic assumptions for stellar evolution, the bolometric luminosity of a star scales as — hence a higher He/H leads to brighter stars. In this Letter, we evolve a series of MESA models to the zero-age main-sequence and highlight that the helium enhancements at the levels measured and inferred for high-redshift galaxies can boost the 1500 A UV luminosity by up to , while simultaneously increasing the stellar effective temperature. The combination of helium enhancements with nebular continuum emission expected for intense bursts of star formation have the potential to help reduce the tension between JWST observations and certain galaxy formation models.
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- Award ID(s):
- 2307599
- PAR ID:
- 10611839
- Publisher / Repository:
- The Open Journal of Astrophysics
- Date Published:
- Journal Name:
- The Open Journal of Astrophysics
- Volume:
- 7
- ISSN:
- 2565-6120
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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