Speaker
Description
The spatial and dynamical distribution of stars are fundamental to characterise galaxy populations across cosmic time. They reflect the conditions in which those stars formed, and the subsequent evolution experienced by their host galaxies. Hence, stellar morphologies represent a fundamental part of our understanding of galaxy formation. Despite several known correlations between the morphology of a galaxy and its internal and environmental properties, identifying their origin is not trivial. In this sense, galaxy simulations provide a complementary input that can help interpret the observed data.
However, most simulations of representative volumes of the universe suffer from several limiting factors when it comes to stellar morphologies. Gas is artificially prevented from reaching low temperatures and high densities in the interstellar medium, "puffing up" the resulting stellar discs. Additionally, the energy transfer between a dynamically cold stellar disc and a hot dark matter halo is spuriously exacerbated due to different dark matter and star particle masses. This can lead to a secular thickening of the already thicker discs, introducing a bias in the predicted galaxy morphologies.
I will present the first galaxy morphology results from the COLIBRE suite of cosmological simulations. COLIBRE solves the aforementioned issues, and makes several improvements to the baryonic physics relevant to galaxy formation, like using a non-equilibrium chemistry model coupled to a live dust model, and a more refined model for energetic feedback. Altogether, the population of galaxies in COLIBRE is realistic across a variety of observed scaling relations and redshifts. Thanks to the large dynamic range in volume ($V \in [25^{3},200^{3},400^{3}]\,\mathrm{Mpc}^3$) and corresponding mass resolution ($m_{\rm p} \in [10^5, 10^6, 10^7]\,\mathrm{M}_{\odot}$) of the simulation suite, this talk will show a statistical study of galaxy morphologies from dwarf galaxies to galaxy clusters that can complement ongoing surveys like Euclid.
