Graph-based deep learning for representing events at the Large Hadron Collider

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Finding appropriate domain specific representations of data is key to solving problems in science. Contemporary machine learning has developed architectures that have proved to be successful in some domains in proposing adaptive hidden variables that capture correlations in large datasets. The Large Hadron Collider (LHC) in CERN is a source of very large datasets that offer the possibility to learn new fundamental physics for which novel pattern recognition tasks need to be developed. The mapping from the known physics to the observations in the detectors of the LHC constitutes the background against which novelties are sought. This mapping requires the necessary translation of physical constraints into machine learning frameworks in order to calibrate data streams and propose optimisation objectives in order to allow modern deep learning methods to offer potential solutions. One of the promising approaches to be explored in this project is the use of graph convolution networks to embed events of detected particle showers treated as point clouds. This will provide the representations of collider events that can be used for processing tasks such as detecting appropriate signals of new physics from background. This project will be jointly supervised with particle physicists from the University of Southampton and Rutherford Appleton Laboratory who have existing links with LHC. Appropriate training in particle physics will be provided as part of the studentship at the start of the project via the NExT PhD School and the RAL Graduate Lectures.