Graph Neural Networks for pattern recognition and Fast Track Finding in LHC data

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In particle physics collider experiments, reconstructing particle trajectories (tracks) in the detector is computationally and intellectually one of the most challenging parts of experimental data analysis. A typical LHC detector contains many thousands of sensors measuring particle positions along their trajectory and the track finding (aka pattern recognition) problem is to associate individual measurements ("hits") into sequences representing particle trajectories. The scale of the problem is enormous, given that the number of hits can be up to 400000 per event; the number of tracks -- several thousands. The current track finding algorithm adopted in the LHC experiments is based on the combinatorial track following which scales non-linearly with the number of hits. The corresponding CPU time increase, typically close to cubical, creates huge and ever-increasing demand for computing power. This drawback motivates investigating novel approaches for track finding, in particular, those based on the machine learning (ML) techniques. The benefits of such an approach could lead to tens of millions of pounds savings in CPU. The aim of this project is to explore track finding methods utilizing a graph-based track model and graph neural networks (GNN). An ML-based algorithm will be developed to iteratively resolve ambiguous connections in the graph network and extract good track candidates, given the input hit features such as a shape of a charge cluster representing a track position measurement in a silicon detector. The excitation/inhibition rules of individual GNN neurons will be designed to facilitate the "simple-to-complex" approach for "hits-to-tracks" association such that the network starts with relatively "easy" areas of an event with low hit density and gradually progresses towards more complex "hot" areas.