Formation and evolution of galaxies2019.07.31 17:23 - admin-bp4
Studying galaxy formation and evolution through cosmic time is a cornerstone of modern cosmology and astrophysics. Moreover, it is one of the most active interdisciplinary fields of science, covering the physical and chemical complexities that a galaxy undergoes during its lifetime, from formation to death.
Galaxies emit radiation across the electromagnetic spectrum, from \gamma rays to radio waves. In BP4, we make use of the extensive multi-wavelength catalogues from the Herschel Extragalactic Legacy Project (HELP), the Low-Frequency Array (LOFAR) and AKARI, covering hundreds of millions of galaxies observed from the UV to radio out to redshift 5, to derive physical properties of the galaxies that they contain (dust attenuation, star-formation rates, stellar masses, etc). We couple these photometric catalogues with spectroscopic data from the Atacama Large (sub)Millimetre Array (ALMA) and VIMOS Public Extragalactic Redshift Survey (VIPERS), which allow us to directly examine galaxies’ interstellar medium (ISM), including metallicities and gas properties. From these data, we can better constrain the physical relations that govern them and the implications these relations have on their evolution. The wealth of data also allows us to examine the most extreme galaxies, such as Ultra-luminous infrared galaxies (ULIRGs). The analysis we perform with these catalogues will also prepare us for the future surveys from the Vera C. Rubin Observatory.
Our work employs state of the art techniques. Using the latest spectral energy distribution (SED) fitting codes, we test the non-universality of dust attenuation laws to derive a more general dust extinction behaviour to better describe the largest possible galaxy sample. Modern image analysis techniques are used to derive the morphological and structural parameters, allowing an exploration into how the form of a galaxy influences its internal processes and how its environment influences its morphology. Cutting edge machine learning techniques are also used to allow us to generate new insights and rapidly process the unprecedented volumes of data that we have now and will have in the future.