Speaker
Description
The intensity of the far-ultraviolet (FUV) interstellar radiation field (G0) in galaxies plays a critical role in dictating the thermal and chemical structure of the interstellar medium (ISM), which in turn is fundamental to regulating the star formation rate (SFR) and the subsequent picture of galaxy evolution that the SFR paints. However, efforts to develop closed-form analytic expressions linking G0 to key physical variables, such as SFR, gas density and turbulence, remain challenging. This is a particularly significant issue in engineering large-scale cosmological simulations such as the Kiara simulations, the next-generation successor of the archetypal Simba simulations, which, whilst boasting enhanced treatments of dust and SF physics, still cannot resolve gas to the scales small enough to follow the radiation hydrodynamics required to evaluate G0 numerically. At present, G0 is roughly estimated in Kiara from the total SFR within each kernel. This method struggles to reproduce sufficiently low dust temperatures, suggesting that this coarse parameterisation of G0 is inaccurate. In response, in this work, we leverage recent advancements in machine learning (ML) and use symbolic regression (SR) techniques to produce the first data-driven analytic expressions for G0 using the high-resolution FIRE-2 galaxy simulation suites that explicitly tracks this quantity for each gas particle. These SPH simulations are ideal for capturing the radiation physics of galaxies because they resolve down to the giant molecular cloud (GMC) scale where massive stars are formed and therefore G0 is regulated. We directly embed the best ML-discovered symbolic expressions as the sub-grid prescription of G0 in Kiara to run new cosmological simulations that specifically investigates the global galaxy-wide and large cosmological-scale effects of embedding local radiation physics. These first-ever SR-informed cosmological simulations thus link multiple physical scales in a computationally efficient manner.