Transfer Learning for Time Domain Astrophysics

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LSST is predicted to discover ~10 million transient and variable objects per night. Accurate classification of these objects will be crucial to allow both population studies and identify rare objects. We propose to move from simply using the photometric data to exploiting the additional information that can be afforded by spectroscopy. Spectroscopy allows a direct probe of the chemical, physical and kinematic conditions in an astrophysical source. However the collection of spectroscopic data is much more time consuming than simple photometry. Recent experiments using traditional statistical methods such as PCA and more novel machine learning approaches have shown some promise that low signal-to-noise ratio observations may be classifiable using more advanced techniques. The PhD project proposes the development and use of more suitable machine learning approaches starting with the use of deep learning (DL) for dimensionality reduction. However interpretability issues are inherent to the nature of DL methods and at present it is not clear how to incorporate prior human knowledge expertise into DL models. By assuming the knowledge is extracted solely from training data, we ignore valuable scientist experience. We plan to address this by exploring the use of Deep Gaussian Processes (DGPs). These can be characterized as deep learning networks without intra-layer connections, where the transformation between layers is probabilistic and modelled with Gaussian processes. DGPs could allow for developing more interpretable models, and the expert knowledge can be incorporated as prior distributions. The output of this research has a strong potential impact in both astrophysics and machine learning. In astrophysics, an improvement in classifiability of low signal to noise ratio spectra allows greater sample sizes to be developed in the same time and improves the chances of early detection of unique objects which can often be the best test of physical theories in extreme environments. For applied mathematics, it will advance the current research into DGP's, a method that is still in development and at the forefront of machine learning research.