We explore the invigorating intersection of astronomy and human-machine collabora-
tion, casting a particular focus on the transformative role these elements play in our
exploration of the universe. The focus of our study is ASKAP and the pivotal role of its
monitoring data in enhancing astronomical discoveries. ASKAP is an innovative radio
telescope array that’s redefining our cosmic mapping capabilities. However, ASKAP’s
success in charting unprecedented amounts of galaxies brings forth the challenge of man-
aging and interpreting the resulting ’data explosion’. In response to this, we explore
the potential in anomaly detection, leading to a proposed collaborative human-machine
approach that maximizes the strengths of both components in effectively managing and
interpreting the vast datasets. We use anomaly detection by recognizing what’s ’normal’, we can identify the anomalies – the unusual occurrences that could lead to new discoveries. We propose a collaborative human-machine approach. Machines process the data and identify anomalies, while humans interpret the results and guide the exploration