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Dr Ivy Wong, a prominent radio astronomer, is at the forefront of revolutionising the way astronomers analyse vast datasets from beyond our galaxy. Her work focuses on leveraging advanced machine learning and artificial intelligence techniques to interpret the enormous amounts of data generated by new-generation radio telescopes, such as the Australian Square Kilometre Array Pathfinder (ASKAP).
After World War II, radar technology was adapted for radio astronomy, allowing the study of stars through low-energy radio waves converted into electrical signals and images. Advancements in radio telescopes like ASKAP have significantly increased data volumes, requiring complex data management similar to sifting through vast amounts of sand to find tiny shells.
ASKAP, a leading radio telescope, repeatedly scans the sky to create extensive maps, capturing over three million galaxies per survey. This detailed data overwhelms traditional analysis methods, necessitating more advanced techniques. Dr Wong is pioneering the use of machine learning and AI to meet this challenge, aiming to uncover the universe’s secrets hidden in the vast data.
ASKAP generates data at an astonishing rate of 100 trillion bits per second, surpassing the data rate of Australia’s entire internet traffic. This immense volume of data is managed by supercomputers like Setonix at the Pawsey Supercomputing Research Centre, which store, calibrate, and transform the information. However, the potential data volume far exceeds current processing capacities, prompting Dr Wong to explore advanced algorithms and machine learning techniques.
Citizen science projects have been one method of analysing astronomical data. Dr Wong worked on an online galaxy classification project that invited the public to identify features in galaxy images. While engaging and educational, these projects are resource-intensive. She believes that advanced algorithms, particularly those used in machine learning, are crucial for capturing as much information as possible from the universe without the need for massive data storage.
Dr Wong’s work at ASKAP and Parkes observatories also underscores the importance of acknowledging the traditional owners of the lands on which these facilities are situated. ASKAP is located in Wajarri Yamaji Country, and the Parkes Observatory is on Wiradjuri land. This recognition is an essential aspect of the collaborative efforts driving scientific advancements.
Dr Wong’s curiosity led her to a summer project at the Parkes radio telescope during her second year at the University of Melbourne, sparking her passion for radio astronomy. She pursued this interest in her PhD by mapping hydrogen gas in galaxies. Now a Science Leader, she collaborates with computer scientists to analyse data from next-generation telescopes, exploring hydrogen atoms and distant galaxies.
Her current focus is on applying machine learning methods to astronomical surveys. This interdisciplinary approach combines computer science and astronomy, bringing together diverse minds to tackle complex problems. Dr Wong’s work is not only advancing astronomical research but also fostering collaboration among experts from different fields.
Dr Wong emphasises the importance of celebrating scientific talent and its contributions to society, highlighting that much of our modern world is built on the foundations of science and technology.
Dr Wong is committed to mentoring students and early career researchers, ensuring that the next generation will continue to innovate in astronomy. She believes that the curious and bright minds of today will build on the methods developed by her team, pushing the boundaries of scientific discovery even further.
As she navigates the intersection of machine learning and radio astronomy, Dr Wong is poised to unlock new cosmic mysteries and drive the field into a new era of discovery. Her work exemplifies how technological innovation and interdisciplinary collaboration can overcome the challenges of analysing astronomical data, revealing the universe’s secrets and advancing our understanding of the cosmos.
