The project was divided in three working packages (WPs).
In WP1, my team and I devised an original machine learning code as a compelling alternative to standard tools used to estimate galaxy physical properties such as redshift (z), stellar mass (M*) and star formation rate (SFR). I built the code starting from the self-organizing maps (SOM) algorithm, using the latest observations in the COSMOS and SXDF fields and also state-of-the-art simulations for calibration and validation. An image showing the layout of one of the two fields (COSMOS) compared to the footprint of HST surveys, is attached to the present Summary. The SOM method is described and applied in two peer-reviewed publications, and has been presented in talks and seminars at different universities and research institutes. WP1 also included the creation of astronomical catalogs, containing optical and infra-red information for millions of galaxies. After being publicly released (with two related articles featuring high-impact scientific journals) these catalogs have been downloaded by 100+ astronomers (and counting).
In WP2 my collaborators and I constrained, for the first time, the stellar mass function (SMF) and the redshift-space 2-point correlation function (2PCF) of high-mass galaxies between z = 3 and 7, to characterize their growth and clustering in a regime that was poorly studied until now. The high-mass regime we probed allowed for a particularly valuable comparison with computer simulations, especially the ones reproducing galaxy evolution in big cosmological boxes since they require as observational counterpart a large galaxy catalog like the one built in WP1. Looking at the number density of massive systems up to high redshift, we found a remarkable degree of agreement between the latest theoretical predications and our data, both showing an excess with respect to more established models of galaxy formation. Such an intriguing feature suggests that latest physical recipes are correctly building up galaxies with up to 10^11 M⊙ less than 2 billion years after the Big Bang. The assembly efficiency is higher than what expected in the classical “AGN feedback” scenario and it is not the result of an ad-hoc calibration of the simulations’ parameters, since before this project there were no data available at the redshift/stellar mass regime for fine tuning. These results are presented in an article submitted for publication to a high-impact journal, and have been already presented during two international meetings.
The achievement of WP3 was the implementation of a “halo model” relating statistical properties of dark matter (in particular the halo mass function) to baryonic properties (especially M*). The model also allows to statistically distinguish central vs. satellite haloes, therefore it provides a more refined description of the universe than the SMF study. The results (presented in a peer-reviewed paper) support the scenario in which the peak of star-formation efficiency moves towards more massive halos at higher redshifts. The comparison with simulations showed that even when theoretical models correctly reproduce the 1-point statistics (see WP2) they still struggle to incorporate environmental mechanisms such as galaxy-galaxy interactions and the gravitational effects of dark matter in the massive regime. In fact, WP3 results now revealed a significant discrepancy as simulations generally have a higher contribution from satellites to the total stellar mass budget than our observations. This means that the feedback mechanisms acting in group- and cluster-scale haloes are likely to be less efficient in quenching the mass assembly of satellites. This part of the project has been completed only recently and it is expected to display its full impact. Nonetheless, I have already been contacted by a few research teams interested in comparing their independent analysis with our findings.
