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Anomaly detection methods for LSST, MeerKAT and the SKA

Project: 
The Large Synoptic Survey Telescope (LSST) will issue 10 million new transient alerts every night while SKA1-Mid will generate over 100PB of data per day. So how do we discover new phenomena in an era where the majority of data will never be seen by a human? Machine learning offers the exciting opportunity for scientific discovery from large datasets. In this project, you will join Dr. Michelle Lochner and Prof. Bruce Bassett, among the few researchers in South Africa who are members of LSST, in developing and applying anomaly detection algorithms to existing and simulated datasets for LSST, MeerKAT and the SKA. Due to the enormous scope of anomaly detection in astronomy, this project can be tailored to the student's specific interests and is offered at both Honours and Masters level.
Node This Project Is Offered On: 
(UCT)

Supervisor

Michelle
Lochner

Co-Supervisor

Bruce
Bassett
Requirements for students to address: 
Excellent python programming skills are a requirement. Knowledge of machine learning is not required although would be an advantage.
Research Area: 
Astronomy


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