Anomaly Detection in Astronomical Data using Machine Learning
2021-10-25, 12:00–12:30, Grand Ballroom

The next generation of telescopes such as the SKA and the Rubin Observatory will produce enormous data sets, far too large for traditional analysis techniques. Machine learning has proven invaluable in handling large data volumes and automating many tasks traditionally done by human scientists. In this talk, I will discuss how machine learning for anomaly detection can help automate the process of locating unusual astronomical objects in large datasets thus enabling new cosmic discoveries. The framework of using active learning to build a recommendation engine for interesting anomalies is general and could be applied in other fields.


Big data: How to deal with the 5 Vs (volume, velocity, variety, veracity, value)