Seb Oliver
Dust obscuration of star formation in the distant Universe
Observations of the changing distribution of star formation rate (SFR) across cosmic time have played a critical role in developing our understanding of galaxy evolution. Following the successes of both Spitzer & Herschel in the far infrared (FIR) and Hubble & GALEX (among others) in the optical/UV, we have a consistent picture of both SFR densities and SFR distribution up to the peak of activity, z < 3, for both obscured and unobscured SFR. However, it is unclear how this picture will develop as we extend from z to z > 4. During that epoch, star formation activity is rapidly increasing, but the role of dust obscuration is very poorly understood. Herschel has demonstrated an unrivalled capacity for discovering dusty systems out to z > 6, but the existing samples are small and probe only the highest luminosities. In this project, you will exploit and develop novel techniques to remove FIR foreground sources, revealing the dust obscuration at high-z. You will then approach the exploration of z > 4 dust from two directions. (1) You will produce new samples of dusty high-z targets which are both larger in number and push to fainter objects than existing samples. Follow-up campaigns (e.g. with ALMA) can then be undertaken. This method exploits the unsurpassable wide-eld coverage of Herschel. (2) You will provide a detailed statistical understanding of the dust emission from high-z galaxies selected in rest-frame optical/UV and their invisible neighbours. Together, these approaches will provide a denitive picture of star formation at high redshift.
Galaxy evolution - a multicomponent machine-learning model
The statistical characterisation of the extra-galactic populations is fundamental to address the evolution of galaxies and AGN, both for empirical comparisons of populations at different epochs and for confronting theoretical models. The extraordinary wealth of data now available from deep multi-wavelength surveys, the questions being posed, and the theoretical models that address them, are rich and complex. However, the statistical measures being used (luminosity functions, template spectral energy distributions, 2-point correlation functions and ad-hoc scaling relations) have changed little since the 1970s. These outdated measures entrench our prejudices and limit our understanding. In this project you will adopt a radical, new approach. Using techniques from machine learning we will build a probabilistic generative model of the vast multi-wavelength catalogue and map data within the Herschel Extragalactic Legacy Project (HELP). This model will provide a robust probabilistic description of the observables, with limited and well dened prior assumptions. You will use this to characterise the key emission components of galaxy populations simultaneously at all wavelengths and the probabilistic relations between them. You will focus on the star forming and AGN components where understanding has been particularly limited by ad-hoc segregation and classication. The full posterior probabilities will be fully characterised providing us with a compact description" of the data. This then allows you to develop a tool that can be used to generate synthetic data sets, consistent with the constraining data, that can then be used to test any physically motivated model of galaxy evolution.
For more information/to apply for these projects, please contact Prof. Seb Oliver (email: S.Oliver [AT] sussex.ac.uk).