Using Artificial Neural Networks to Constrain the Halo Baryon Fraction
  during Reionization
  Radiative feedback from stars and galaxies has been proposed as a potential
solution to many of the tensions with simplistic galaxy formation models based
on $\Lambda$CDM, such as the faint end of the UV luminosity function. The total
energy budget of radiation could exceed that of galactic winds and supernovae
combined, which has driven the development of sophisticated algorithms that
evolve both the radiation field and the hydrodynamical response of gas
simultaneously, in a cosmological context. We probe self-feedback on galactic
scales using the adaptive mesh refinement, radiative transfer, hydrodynamics,
and $N$-body code. Unlike previous studies which assume a homogeneous UV
background, we self-consistently evolve both the radiation field and gas to
constrain the halo baryon fraction during cosmic reionization. We demonstrate
that the characteristic halo mass with mean baryon fraction half the cosmic
mean, $M_{\mathrm{c}}(z)$, shows very little variation as a function of
mass-weighted ionization fraction. Furthermore, we find that the inclusion of
metal cooling and the ability to resolve scales small enough for self-shielding
to become efficient leads to a significant drop in $M_{\mathrm{c}}$ when
compared to recent studies. Finally, we develop an Artificial Neural Network
that is capable of predicting the baryon fraction of haloes based on recent
tidal interactions, gas temperature, and mass-weighted ionization fraction.
Such a model can be applied to any reionization history, and trivially
incorporated into semi-analytical models of galaxy formation.
