Triple success for astronomy researchers
Congratulations to three researchers: Dr Adam Carnall, Dr Catherine Hale and Dr Tilman Troester, who have been awarded Early Career Fellowships.
The Leverhulme Trust Early Career Fellowships are intended to assist those at a relatively early stage of their academic careers to undertake a significant piece of publishable work.
All three researchers are based in the School’s Institute for Astronomy.
Adam will be investigating the metal contents of very distant massive galaxies, observed as they were 9 billion years ago (4 billion years after the Big Bang). This information will provide critical insights into the most important processes driving their evolution. He will use data from his recent 8-night observing programme, using the European Southern Observatory's Very Large Telescope at Paranal Observatory, Chile.
Dark matter influence
Catherine will be using the properties and clustering of galaxies from state-of-the-art, deep observational surveys at radio frequencies to investigate how the evolution of galaxies is influenced by their dark matter environments. Radio surveys are particularly useful for her work as they can observe galaxies across large periods of the history of the Universe without obscuration from intervening dust. Extragalactic radio surveys also observe two interesting galaxy populations which may evolve differently: those which are forming stars and those which host powerful accreting supermassive black holes.
What is the origin of the accelerated expansion of the Universe? The coming decade will see the advent of a new generation of galaxy surveys that will measure the positions and shapes of billions of galaxies and help us answer this. The full exploitation of these data sets is however restricted by our limited understanding of the non-linear processes that contribute to the formation of galaxies, as well as the insufficient sophistication of our statistical inference methodologies. Tilman will pursue a multidisciplinary research programme at the interface of cosmology, machine learning, and statistics. In order to extract the full non-linear information contained within galaxy surveys he will analyse the clustering of galaxies using novel deep learning (DL) techniques. These techniques will then be adapted to efficiently account for galaxy formation in dark matter-only simulations. To enable robust statistical inference on these massive data sets, he will develop methods to efficiently estimate posteriors in high-dimensional parameter spaces.