Studying supermassive black holes with the next-generation radio surveys

Ivy Wong1,2

1CSIRO Astronomy & Space Science, PO Box 1130, Bentley, WA 6102
2ICRAR-M468 UWA, 35 Stirling Hwy, Crawley, WA 6009

 

Abstract:

Supermassive black holes lurk in the heart of most if not all galaxies. As galaxies grow and evolve, so do these central black holes. Jets of relativistic charged particles (synchrotron emission) can be launched through the process of matter being accreted into the black holes. Over time, the jets fade and leave behind lobes of non-relativistic particles that remain at large distances from their originating galaxy. Such galaxies with radio jets and/or lobes are known as radio galaxies. Several fundamental questions such as: 1) the driving mechanisms that triggers jet emission; and 1) the location relative to the central black hole where the jet is launched, remain largely unanswered. Large samples are required to constrain our understanding of the formation and evolution of radio galaxies. Unfortunately, the traditional method for cataloguing radio galaxies is through visual inspection. In this talk, I will describe our foray into crowd-sourcing and citizen science through projects such as Galaxy Zoo and Radio Galaxy Zoo. While crowdsourcing provides a significant improvement in source classification efficiency, this improvement is still insufficient to handle the millions of sources that we expect from the upcoming Evolutionary Map of the Universe (EMU) survey using the Australian Square Kilometre Array Pathfinder (ASKAP) currently being commissioned in Western Australia. To this end, I will describe some of our research on developing automated classifiers based on a range of deep learning methods that have been trained on the initial results of the Radio Galaxy Zoo project.


Biography:

Dr Ivy Wong is a radio astronomer and a CSIRO Science Leader working on massive data challenges in the era of the Square Kilometre Array at CSIRO Astronomy & Space Science in Perth, WA. Using large all-sky radio surveys, Ivy studies how galaxies form stars; how central supermassive black holes grow (AGN) and how AGN affect the star formation history and evolution of a galaxy. Ivy’s research interests also include non-traditional data analysis methods such as the exploration of citizen science and the potential applications of deep learning algorithms.
The next-generation radio telescopes begin to survey wider, deeper and further back in the Universe’s history, astronomers will enter the massive data era when traditional methods of analyses will be severely tested. Ivy obtained her PhD (Astrophysics) in 2008 from the University of Melbourne and has previously worked at Yale University, CSIRO and the International Centre Radio Astronomy Research (UWA).

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