Romeel Davé

Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations are a core constituent of modern cosmology, while the required computations are both expensive and time-consuming. At the same time, the comparatively fast simulation of dark matter requires fewer resources, which has led to the emergence of machine learning algorithms for baryon inpainting as an active area of research; here, recreating the scatter found in hydrodynamic simulations is an ongoing challenge. This paper presents the first application of physics-informed neural networks to baryon inpainting by combining advances in neural network architectures with physical constraints, injecting theory on baryon conversion efficiency into the model loss function. We also introduce a punitive prediction comparison based on the Kullback–Leibler divergence, which enforces scatter reproduction. By simultaneously extracting the complete set of baryonic properties for the simba suite of cosmological simulations, our results demonstrate improved accuracy of baryonic predictions based on dark matter halo properties and successful recovery of the fundamental metallicity relation, and retrieve scatter that traces the target simulation’s distribution.

Radio-mode feedback is a key ingredient in galaxy formation and evolution models, required to reproduce the observed properties of massive galaxies in the local Universe. We study the cosmic evolution of radio-active galactic nuclei (AGN) feedback out to z ∼ 2.5 using a sample of 9485 radio-excess AGN. We combine the evolving radio luminosity functions with a radio luminosity scaling relationship to estimate AGN jet kinetic powers and derive the cosmic evolution of the kinetic luminosity density, Ωkin (i.e. the volume-averaged heating output). Compared to all radio-AGN, low-excitation radio galaxies dominate the feedback activity out to z ∼ 2.5, with both these populations showing a constant heating output of $\Omega _{\rm {kin}} \approx (4\!-\!5) \times 10^{32}\, \rm {W\, Mpc^{-3}}$ across 0.5 < z < 2.5. We compare our observations to predictions from semi-analytical and hydrodynamical simulations, which broadly match the observed evolution in Ωkin, although their absolute normalization varies. Comparison to the Semi-Analytic Galaxy Evolution (sage) model suggests that radio-AGN may provide sufficient heating to offset radiative cooling losses, providing evidence for a self-regulated AGN feedback cycle. We integrate the kinetic luminosity density across cosmic time to obtain the kinetic energy density output from AGN jets throughout cosmic history to be $\sim 10^{50}\, \rm {J\, Mpc^{-3}}$. Compared to AGN winds, the kinetic energy density from AGN jets dominates the energy budget at z ≲ 2; this suggests that AGN jets play an important role in AGN feedback across most of cosmic history.