IAU 311: Galaxy Masses as Constraints of Formation Models

In the era of precision cosmology, we think we can accurately predict the distribution of dark matter in the Universe. However the impact of baryonic physics is still largely unknown and our understanding of galaxy formation must rely on observations. A key advance in recent years has been the ability to enrich studies of the luminosity evolution of galaxies with determinations of their stellar or total masses from dynamical analyses using stellar populations, stellar or gaseous dynamical models, weak or strong lensing. Contrary to the light distribution alone, the distribution of both the stellar and dark matter in galaxies can be robustly compared to galaxy formation models.

Dynamical studies of galaxies near and far have evolved from modelling the mass distribution of individual objects to capitalizing on large surveys using integral field and multi-object spectroscopy, strong or weak gravitational lensing, planetary nebulae, stellar and gas kinematics, and multi-wavelength studies, to constrain masses from the stellar population. Much of this progress has relied on key instrumentation developments. For instance, new spectrographs optimized to near-infrared wavelengths now better trace the rest-frame visual spectra of distant galaxies. Massive multi-objects capabilities also allow larger samples to be obtained in feasible exposure times. In the foreseeable future, 30-40-m class telescopes, the LSST survey and JWST and EUCLID missions promise to extend our studies of galaxy masses and kinematics of nearby galaxies up to redshift z~2 and beyond, where most of the galaxy assembly has taken place.

This symposium aims at bringing together galaxy evolution theorists, observers of the nearby and distant universe, and instrumentation specialists. We must identify what key observables can be robustly reproduced by the models, how the existing and new instrumentation should be optimized for galaxy evolution studies, and what future observations would be most useful to constrain the models.