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.

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.