Understanding how galaxies form, interact, and evolve comes largely from comparing theory predictions with observational data. Numerical simulations of galaxies provide the most accurate approach... Show moreUnderstanding how galaxies form, interact, and evolve comes largely from comparing theory predictions with observational data. Numerical simulations of galaxies provide the most accurate approach to testing the theory, as they follow the non-linear evolution of gas and dark matter in great detail and incorporate numerous baryonic processes, among which are energy feedback from supernovae (SNe) and Active Galactic Nuclei (AGN). In this thesis, we show the results of the development of the new model COLIBRE for cosmological simulations of galaxy formation that include a cold interstellar medium. First, we present a new SN feedback recipe developed for COLIBRE, whereby SN energy is injected into the gas in thermal and kinetic forms, and the total energy and momentum of the system of gas and stars are exactly conserved. Second, we conduct a detailed comparison of different ways in which SN energy is distributed in the gas environment around young stellar populations. Third, by using our simulation setup originally developed to test COLIBRE’s SN feedback, we show that the radioactive isotope Fe60 that has been detected on Earth is likely of SN origin. Finally, we present the calibration of the SN and AGN feedback of the COLIBRE model using machine learning. Show less
