The merger-driven evolution of early-type galaxies and the connection with their dark matter halos

Massive early-type galaxies (ETGs) are "red and dead" systems mainly composed by old and metal-rich stellar populations. In a cosmological context, present-day ETGs are believed to be the remnants of a complex stellar mass assembly history marked by several mergers, which are the consequence of the underlying hierarchical assembly of their host dark matter halos. This Ph.D. thesis deals mainly with the merger-driven evolution of ETGs, studying how scaling relations evolve across cosmic time, how the stellar populations of the progenitors settled into the remnant galaxies, and how the mass of the host dark matter halos can be inferred from other galaxy properties. In the first part, I will present the results of the first systematic study in the literature of the evolution of the scaling relation that links the stellar mass of ETGs to their stellar velocity dispersion. By exploiting a Bayesian hierarchical formalism, I tested different functional forms to investigate how the stellar mass–velocity dispersion relation may vary as a function of redshift. In the second part, I will illustrate a comparison between observed ETGs from the MaNGA survey and simulated galaxies from the cosmological simulation suite IllustrisTNG. The aim of this project is to provide an interpretative scenario of the stellar mass assembly history of present-day ETGs, comparing the radial distributions of their stellar properties with those of simulated galaxies, in which we can disentangle the role of stars formed in situ and stars formed ex situ. Finally, I will exhibit the preliminary results of a project aimed at inferring the dark matter mass of halos hosting galaxies of the IllustrisTNG simulation, using Explainable Boosting Machine. I will show the strength of this new machine learning method that allows us to provide a prediction for the dark matter halo mass using several galaxy properties.