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Radiative feedback from stars and galaxies has been proposed as a potentialsolution to many of the tensions with simplistic galaxy formation models basedon $\Lambda$CDM, such as the faint end of the UV luminosity function. The totalenergy budget of radiation could exceed that of galactic winds and supernovaecombined, which has driven the development of sophisticated algorithms thatevolve both the radiation field and the hydrodynamical response of gassimultaneously, in a cosmological context. We probe self-feedback on galacticscales using the adaptive mesh refinement, radiative transfer, hydrodynamics,and $N$-body code. Unlike previous studies which assume a homogeneous UVbackground, we self-consistently evolve both the radiation field and gas toconstrain the halo baryon fraction during cosmic reionization. We demonstratethat the characteristic halo mass with mean baryon fraction half the cosmicmean, $M_{\mathrm{c}}(z)$, shows very little variation as a function ofmass-weighted ionization fraction. Furthermore, we find that the inclusion ofmetal cooling and the ability to resolve scales small enough for self-shieldingto become efficient leads to a significant drop in $M_{\mathrm{c}}$ whencompared to recent studies. Finally, we develop an Artificial Neural Networkthat is capable of predicting the baryon fraction of haloes based on recenttidal interactions, gas temperature, and mass-weighted ionization fraction.Such a model can be applied to any reionization history, and triviallyincorporated into semi-analytical models of galaxy formation.

Using Artificial Neural Networks to Constrain the Halo Baryon Fraction during Reionization