Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes. (arXiv:2012.05820v2 [astro-ph.GA] UPDATED)

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 …

arxiv astro dark matter hybrid machine property