Browsing by Author "Cowan P"

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    Asteroid Lightcurves from the MOA-II Survey: A pilot study
    (Oxford University Press on behalf of Royal Astronomical Society, 2022-08) Cordwell AJ; Rattenbury NJ; Bannister MT; Cowan P; Abe F; Barry R; Bennett DP; Bhattacharya A; Bond IA; Fujii H; Fukui A; Itow Y; Silva SI; Hirao Y; Kirikawa R; Kondo I; Koshimoto N; Matsubara Y; Matsumoto S; Muraki Y; Miyazaki S; Okamura A; Ranc C; Satoh Y; Sumi T; Suzuki D; Tristram PJ; Toda T; Yama H; Yonehara A
    The Microlensing Observations in Astrophysics (MOA-II) survey has performed high cadence, wide field observations of the Galactic Bulge from New Zealand since 2005. The hourly cadence of the survey during eight months of the year, across nearly 50 deg2 of sky, provides an opportunity to sample asteroid lightcurves in the broad MOA-R filter. We perform photometry of a subset of bright asteroids numbered observed by the survey. We obtain 26 asteroid rotation periods, including for two asteroids where no prior data exist, and present evidence for the possible non-principal axis rotation of (2011) Veteraniya. This archival search could be extended to several thousands of asteroids brighter than 22nd magnitude.
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    Towards asteroid detection in microlensing surveys with deep learning
    (Elsevier B.V., 2023-01-30) Cowan P; Bond IA; Reyes NH
    Asteroids are an indelible part of most astronomical surveys though only a few surveys are dedicated to their detection. Over the years, high cadence microlensing surveys have amassed several terabytes of data while scanning primarily the Galactic Bulge and Magellanic Clouds for microlensing events and thus provide a treasure trove of opportunities for scientific data mining. In particular, numerous asteroids have been observed by visual inspection of selected images. This paper presents novel deep learning-based solutions for the recovery and discovery of asteroids in the microlensing data gathered by the MOA project. Asteroid tracklets can be clearly seen by combining all the observations on a given night and these tracklets inform the structure of the dataset. Known asteroids were identified within these composite images and used for creating the labelled datasets required for supervised learning. Several custom CNN models were developed to identify images with asteroid tracklets. Model ensembling was then employed to reduce the variance in the predictions as well as to improve the generalization error, achieving a recall of 97.67%. Furthermore, the YOLOv4 object detector was trained to localize 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 survey over the years. The methodologies developed can be adapted for use by other surveys for asteroid recovery and discovery.