The Japan/New Zealand/US Microlensing Observations in Astrophysics (MOA) collaboration has been engaged with the detection of exoplanets via gravitational microlensing of over 25 years. This work has resulted in a large data archive of high cadence lightcurves of millions of objects. Many minor planets are also observed in the MOA fields. A sample of minor planet light curves has been derived from the MOA image data archive. This sample is ready for analysis. A starting point would be to perform an unsupervised classification process on this sample. This will result in the lightcurves being sorted into a number of distinct classes. The lightcurves in each class can be inspected for evidence of one or more interesting physical phenomena. These include: Active asteroids, where the asteroid's trajectory and/or rotation is modified by outgassing; slow-rotating asteroids, which are rare and poorly understood; asteroids displaying evidence of the YORP effect.

The candidate must have experience in:

• Python programming
• Basic astronomy and astrophysics
• Experimental physics

Ideally, the candidate would have experience in:

• Machine Learning
• Signal processing, especially time series analysis, fourier analysis
• Data modelling

The PhD candidate will undertake one or more of the following tasks.

• Unsupervised clustering of asteroid and minor planet lightcurves in the MOA data archive
• Categorisation of lightcurve classes based on class characteristics
• Identification of slow rotator asteroids
• Identification of active asteroids
• Kinematic and positional investigation of solar system objects found.