Mr Gary Segal1,3, Dr David Parkinson1,2, Professor Ray Norris3,4
1University Of Queensland, ST LUCIA, Australia,
2Korea Astronomy and Space Science Institute, Daejeon , Korea,
3CSIRO Astronomy and Space Science, Epping, Australia,
4Western Sydney University, Penrith South, Australia
The volume of data that will be produced by the next generation of astrophysical instruments represents a significant opportunity for making unplanned and unexpected discoveries. Conversely, finding unexpected objects or phenomena within such large volumes of data presents a challenge that may best be solved using computational and statistical approaches. We present the application of a coarse-grained complexity measure for identifying interesting observations in large datasets. This measure, which has been termed apparent complexity, has been shown to model human intuition and perceptions of complexity. Unlike supervised learning approaches it does not learn the specific features associated with known interesting observations positioning the approach as an ideal candidate for identifying the unexpected. The approach is computationally efficient and fast making it an ideal candidate for processing very large datasets. We show using data from the Australia Telescope Large Area Survey (ATLAS) that the approach can be used to distinguish between images of galaxies which have been classified as having simple and complex morphologies.
Gary Segal is a PhD student at the University of Queensland and an Australia Telescope National Facility graduate student conducting research at the intersection of statistics, computer science and astronomy. Gary also has a professional background as a quantitative analyst. He finds particular delight in connecting deep theory with practical applications.