Page 45 - University of Pretoria RESEARCH REVIEW 2018

Prize in 1974 for his efforts, which have made next-generation telescopes like the MeerKAT and the Square Kilometre Array (SKA) possible. In the case of the EHT, it has required GPS receivers and atomic clocks at each station to time- stamp data to nanosecond accuracy before being recorded to disk and transported to a supercomputer via
an aircraft as global internet networks are simply not fast enough to transfer the 4 Petabytes of data produced, particularly from the South Pole.
Simulations of this global instrument consumed all the research efforts of Professor Deane and his research group in 2018. Looking ahead, it is anticipated that the contributions of Deane and his group would be used as a predictive engine to optimise the placement of future EHT sites, including potential African stations.
Simulations of the black hole produced by Roger Deane: Goddi, C., et al. 2018, International Journal of Modern Physics, 26, 2.
MeerKAT: a world-leading big data radio telescope
In contrast to the Event Horizon Telescope’s detailed study of just a handful of carefully selected supermassive black holes, South Africa’s MeerKAT telescope will study millions upon millions of them. If the EHT were to be likened to an electron microscope, the MeerKAT would be a wide-field camera.
Built and operated by the South African Radio Astronomy Observatory, and launched in July 2018, the MeerKAT telescope is a precursor to the SKA, which will be the largest scientific facility on Earth.
The MeerKAT is already a world-beating telescope in its own right. By combining light from 64 antennas in the Karoo, it is able to produce far deeper and higher quality images of the radio sky than have been produced before at these observing frequencies. This presents both an opportunity and a challenge to radio astronomers: it provides unprecedented views of the universe, but at the cost of a staggering data set size and richness. To tackle this, Roger Deane’s astrophysics research group has partnered with the well-established Computational Intelligence Research Group (CIRG), led by Dr Christopher Cleghorn in the Department of Computer Science, to use machine learning techniques in the analysis of radio interferometric data.
The two groups have a joint research grant and postgraduate students from the Inter- university Institute for Data Intensive Astronomy, which helps to build cross-disciplinary capacity to address the challenges that these next-generation telescopes present.
43