Using Bayesian Machine Learning to Study Type Ia Supernovae

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My PhD project will involve applying cutting-edge machine learning to big data astronomy. I will work on Bayesian models for Type Ia supernovae, supervised by Dr Kaisey Mandel. Type Ia supernovae are standard candles meaning if we have a model for how bright they are, we can determine their distance. These distances can be combined with galaxy redshifts to put constraints on the expansion and age of the Universe using Hubble's Law. The models are perfect for machine learning as they vary in time as well as wavelength, meaning they are high-dimensional. Taking a hierarchical Bayesian approach to this problem allows us to make inferences on other physics models such as dust extinction laws. There are upcoming telescope missions that will provide us with discoveries of unprecedented amounts of supervnoae detections. The Type Ia models need to be scalable to processes the large number of detections, which is why my work will look towards using machine learning methods rather than traditional methods such as Markov Chain Monte Carlo. Further to this, there will be more spectroscopic (high resolution at all wavelengths) supernovae measurements which will provide opportunity to refine our models. It will be my responsibility in the group to find data-efficient machine learning methods to process this high resolution data.