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Theoretical models of galaxy formation and evolution are primarily investigated through cosmological simulations and semi-analytical models. The former method consumes$\mathcal{O}\left({10}^6\right)$core-hours explicitly modeling the dynamics of the galaxies, whereas the latter method only requires$\mathcal{O}\left({10}^3\right)$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.

Cosmic reionization is likely driven by UV starlight emanating from the first generations of galaxies. A galaxy’s UV escape fraction, or the fraction of photons escaping from the galaxy, is useful to quantify its contribution to reionization. However, the UV escape fraction is notoriously difficult to predict due to local environment dependency and variability over time. Using data from the Renaissance Simulations, we attempt to make predictions about the impact of the first stars and galaxies on their environments. We present a time-independent classification model using a general artificial neural network architecture to predict the UV escape fraction given other galaxy properties—namely halo mass, stellar mass, redshift, star formation rate, lookback time, and gas fraction. We find our validation accuracy to be approximately 50%–65%, depending on the data set size from each zoom-in region of the Renaissance Simulations.

Early photometric results from JWST have revealed a number of galaxy candidates above redshift 10. The initial estimates of inferred stellar masses and the associated cosmic star formation rates are above most theoretical model predictions up to a factor of 20 in the most extreme cases, while this has been moderated after the recalibration of NIRCam and subsequent spectroscopic detections. Using these recent JWST observations, we use galaxy scaling relations from cosmological simulations to model the star formation history to very high redshifts, back to a starting halo mass of 107 M⊙, to infer the intrinsic properties of the JWST galaxies. Here, we explore the contribution of supermassive black holes, stellar binaries, and an excess of massive stars to the overall luminosity of high-redshift galaxies. Despite the addition of alternative components to the spectral energy distribution, we find stellar masses equal to or slightly higher than previous stellar mass estimates. Most galaxy spectra are dominated by the stellar component, and the exact choice for the stellar population model does not appear to make a major difference. We find that four of the 12 high-redshift galaxy candidates are best fit with a non-negligible active galactic nuclei component, but the evidence from the continuum alone is insufficient to confirm their existence. Upcoming spectroscopic observations of z > 10 galaxies will confirm the presence and nature of high-energy sources in the early Universe and will constrain their exact redshifts.

Forward-modeling observables from galaxy simulations enables direct comparisons between theory and observations. To generate synthetic spectral energy distributions (SEDs) that include dust absorption, re-emission, and scattering, Monte Carlo radiative transfer is often used in post-processing on a galaxy-by-galaxy basis. However, this is computationally expensive, especially if one wants to make predictions for suites of many cosmological simulations. To alleviate this computational burden, we have developed a radiative transfer emulator using an artificial neural network (ANN), ANNgelina, that can reliably predict SEDs of simulated galaxies using a small number of integrated properties of the simulated galaxies: star formation rate, stellar and dust masses, and mass-weighted metallicities of all star particles and of only star particles with age <10 Myr. Here, we present the methodology and quantify the accuracy of the predictions. We train the ANN on SEDs computed for galaxies from the IllustrisTNG project’s TNG50 cosmological magnetohydrodynamical simulation. ANNgelina is able to predict the SEDs of TNG50 galaxies in the ultraviolet (UV) to millimetre regime with a typical median absolute error of ∼7 per cent. The prediction error is the greatest in the UV, possibly due to the viewing-angle dependence being greatest in this wavelength regime. Our results demonstrate that our ANN-based emulator is a promising computationally inexpensive alternative for forward-modeling galaxy SEDs from cosmological simulations.