Early-type galaxies: instructions to build them through mergers
Massive early-type galaxies (ETGs) are "red and dead" systems mainly composed of 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. In this talk, I will deal mainly with the merger-driven evolution of ETGs. Firstly, I will illustrate a comparison between observed ETGs from the MaNGA survey and simulated galaxies from the IllustrisTNG cosmological simulation suite. The aim of this study is to provide an interpretative scenario of the stellar mass assembly history of observed present-day ETGs, comparing the radial distributions of their stellar properties with those of simulated galaxies, in which it is possible to disentangle the contribution of stars formed in situ (i.e. within the main progenitor galaxy) and stars formed ex situ (i.e. in other galaxies) and then accreted through mergers. Then, I will describe how the scaling relation between the stellar mass and stellar velocity dispersion in ETGs evolves across cosmic time. Specifically, by extending the results of Cannnarozzo, Sonnenfeld & Nipoti (2020), I model the aforementioned relation through a Bayesian hierarchical approach, considering ETGs with log(M∗/M⊙) > 9 over the redshift range 0 ≲ z ≲ 4. Together with a new characterisation of the relation, I reconstruct the back-in-time evolutionary pathways of individual ETGs on the stellar mass-velocity dispersion plane to answer the question “how did high-redshift ETGs assemble through cosmic time to reach the functional form of the relation in the present-day Universe?“.
After the main topic, if time permits, I would like to spend a few minutes presenting another extra content (below you can find the title and a brief abstract of this further content). Feel free to include it or not in the announcement mail.
EXTRA - Inferring the Dark Matter halo mass in galaxies from other observables with Machine Learning
In the context of the galaxy-halo connection, it is widely known that the Dark Matter (DM) halos show correlations with some physical properties of the hosted galaxy: the most well-known relation is the so-called Stellar-to-Halo-Mass Relation. However, we know that there are several other empirical relations among galaxy properties, involving, for example, the stellar mass, the gas and stellar metallicities, the black hole mass, etc. Given the complexity of the problem and the high number of galaxy properties that might be related to DM halos, the study of the galaxy-halo connection can be approached by relying on machine learning techniques to shed light on this intricate network of relations. With the aim of inferring the DM halo mass and then finding a unique functional form able to link the halo mass to other observables in real galaxies, I rely on the state-of-the-art Explainable Boosting Machine, a novel implementation of generalised additive models with pairwise interactions, training a model on the IllustrisTNG simulation suite at different redshift.