Browsing by Author "Cowan, Preeti"

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    Deep learning for asteroid detection in large astronomical surveys : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand
    (Massey University, 2022) Cowan, Preeti
    The MOA-II telescope has been operating at the Mt John Observatory since 2004 as part of a Japan/NZ collaboration looking for microlensing events. The telescope has a total field of view of 1.6 x 1.3 degrees and surveys the Galactic Bulge several times each night. This makes it particularly good for observing short duration events. While it has been successful in discovering exoplanets, the full scientific potential of the data has not yet been realised. In particular, numerous known asteroids are hidden amongst the MOA data. These can be clearly seen upon visual inspection of selected images. There are also potentially many undiscovered asteroids captured by the telescope. As yet, no tool exists to effectively mine archival data from large astronomical surveys, such as MOA, for asteroids. The appeal of deep learning is in its ability to learn useful representations from data without significant hand-engineering, making it an excellent tool for asteroid detection. Supervised learning requires labelled datasets, which are also unavailable. The goal of this research is to develop datasets suitable for supervised learning and to apply several CNN-based techniques to identify asteroids in the MOA-II data. Asteroid tracklets can be clearly seen by combining all the observations on a given night and these tracklets form the basis of the dataset. Known asteroids were identified within the composite images, forming the seed dataset for supervised learning. These images were used to train several CNNs to classify images as either containing asteroids or not. The top five networks were then configured as an ensemble that achieved a recall of 97.67%. Next, the YOLO object detector was trained to localise asteroid tracklets, achieving a mean average precision (mAP) of 90.97%. These trained networks will be applied to 16 years of MOA archival data to find both known and unknown asteroids that have been observed by the telescope over the years. The methodologies developed can also be used by other surveys for asteroid recovery and discovery.