Office: Kavli K22
Office Tel: (01223) 337519
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Research Keywords: Dark Energy Survey, Black Holes, SDSS, UKIDSS, Galaxies, VISTA, Galaxy Formation/Evolution, VISTA Hemisphere Project, High Redshift, IGM, Infrared, Instruments, Observations, Optical, Quasars, Spectroscopy, Sub-mm, Surveys
The main focus is in the area of galaxy formation and evolution at the highest redshifts possible, especially in the Epoch of Reionization, focusing on the discovery of new high redshift galaxies and quasars that host supermassive black holes, determination of their space densities, star formation rates and how and when they form.
This research also includes developing experimental survey techniques for discovering new high redshift galaxies and quasars; determining how their space density and properties evolve with cosmic time as the Universe evolves; using the quasars to probe the baryonic content of the Universe. My group has pioneered the use of high redshift quasars to determine the mass of neutral hydrogen in the high redshift Universe via intervening absorption lines imprinted on the spectra of background high redshift quasars. I have also pioneered the use of mm and submm microwave radiation to determine the star formation rate in quasar host galaxies.
I am Principal Investigator (PI) of the ambitious VISTA Hemisphere Survey(VHS) which is a new near Infra-Red sky survey project which started in April, 2010. The has been been awarded 300+ clear nights over a 5 year period on the new 4.2m ESO VISTA telescope in Chile. I lead the quasar science working group in the Dark Energy Survey(DES) project which is building a very large CCD camera (DECAM) and has been awarded 500 nights on US CTIO 4m telescope in Chile to use this camera to observe in the optical the VHS region of the sky.
The exploitation of the extremely large datasets from these new surveys requires the use and development of robust data-mining techniques for distributed databases; machine-learning techniques such as decision trees, supervised learning and multi-dimensional data visualisation techniques.