Programme by Session

While cosmological dark matter-only simulations relying solely on gravitational effects are comparably fast to compute, baryonic properties in simulated galaxies require complex hydrodynamical simulations that are computationally costly to run. To solve this issue, we merge an extended version of the equilibrium model, an analytic formalism describing the evolution of the stellar, gas, and metal content of galaxies, into a deep learning framework and retrieve fully Bayesian posteriors for baryon cycling parameters. In doing so, we are able to recover more properties than the analytic formalism alone can provide, creating a high-speed hydrodynamical simulation emulator that populates galactic dark matter halos in N-body simulations with baryonic properties. Our results demonstrate that this novel hybrid system enables the fast completion of dark matter-only simulations by accurately mimicking full hydrodynamical suites of choice, offering an orders-of-magnitude acceleration of commonly deployed simulations in cosmology.