The University's research into Astrophysics covers a wide range of modern astrophysics. We have particular expertise in radio-mm observational astronomy but we also make observations at a wide range of other wavelengths and combine these with theory and modelling.
The Pulsars and Time Domain Astrophysics group are using innovative methods for processing high data volumes in radio astronomy, this includes energy efficient computing, heterogeneous computing architecture and optimised algorithms. The group are also generating machine learning approaches to filter the results of their analyses.
Ben's primary research interests include radio pulsars, neutron stars and rapid radio transients. He's a co-PI of the pulsars and fast transient project TRAPUM which will run on the MeerKAT Telescope a precursor to the Square Kilometer Array (SKA) radio telescope and is involved in the specification of various aspects of the SKA itself. The group is currently undertaking the design work for the pulsar and fast transient search capabilities of the SKA.
Ben has previously collaborated with the Machine Learning and Optimisation group. Currently, he is working on generating machine learning approaches to the data so they are able to filter the amount of data generated to a manageable and useful level and allow for accurate decision making in real time on the streamed data.
Rowan performs large numerical simulations of gas in galaxies as part of her STFC Ernest Rutherford fellowship at the University of Manchester. She develops modules for the physics of the Interstellar Medium in the magnetohydrodynamic code AREPO alongside her collaborators. AREPO is particularly advantageous for studying how stars form in galaxies as it utilizes a moving Voronoi mesh that flows with the gas and allows regions of high density contrast to be resolved efficiently. Using this, Rowan studies how stars form in different galactic environments. Such simulations must be massively parallel to run in a reasonable time frame and necessitate the use of super-computers such as the 6700 core DiRAC Data-Centric Cluster. They also require the development of efficient analysis tools to analyse the large quantities of simulated data produced.