Understanding when and how stellar mass forms over cosmic time is critical to developing a complete picture of galaxy evolution. However, the process of converting from observed photometry to stellar mass is non-trivial: galaxies frequently host multiple photometric sub-structures with formation histories and chemical abundances that vary significantly both internally and from galaxy to galaxy. While multi-wavelength imaging can help disentangle some of these effects, large band-to-band differences in depth and spatial resolution mean that one typically has to choose between broad wavelength coverage and spatial resolution. These complications fundamentally limit our ability to accurately derive stellar masses and formation histories, and often preclude more detailed separation of substructures like bulges and disks.
In an era where multi-wavelength photometric datasets are becoming increasingly commonplace, being able to effectively and efficiently extract information from those data is key. In this project, the student will develop a new approach to measuring stellar masses based on forward modelling multi-band photometric data, and use this new method to study the evolution of detailed galaxy properties over cosmic time. They will initially use publicly-available data from the Sloan Digital Sky Survey (SDSS) and Galaxy And Mass Assembly survey (GAMA), with the possibility of incorporating more detailed stellar population synthesis models or integral field spectroscopic (IFS) data depending on their interest.
This project does not require any special skills, except for some basic familiarity with Python or other programming languages.
For more information, please contact the project supervisor.