Hybrid analytic and machine-learned baryonic property insertion into
  galactic dark matter haloes
  While cosmological dark matter-only simulations relying solely on
gravitational effects are comparably fast to compute, baryonic properties in
simulated galaxies require complex hydrodynamic simulations that are
computationally costly to run. We explore the merging of an extended version of
the equilibrium model, an analytic formalism describing the evolution of the
stellar, gas, and metal content of galaxies, into a machine learning framework.
In doing so, we are able to recover more properties than the analytic formalism
alone can provide, creating a high-speed hydrodynamic simulation emulator that
populates galactic dark matter haloes in N-body simulations with baryonic
properties. While there exists a trade-off between the reached accuracy and the
speed advantage this approach offers, our results outperform an approach using
only machine learning for a subset of baryonic properties. We demonstrate that
this novel hybrid system enables the fast completion of dark matter-only
information by mimicking the properties of a full hydrodynamic suite to a
reasonable degree, and discuss the advantages and disadvantages of hybrid
versus machine learning-only frameworks. In doing so, we offer an acceleration
of commonly deployed simulations in cosmology.
