Computer vision and machine learning for pattern recognition in LHC data

The current track finding algorithms adopted in the LHC experiments are based on the combinatorial track following. The combinatorial stage of the algorithm combines hits from a subset of sensors into short track segments called seeds. The track following stage traces each seed through the detector volume and picks up hits belonging to a seeded track. As the seed number scales non-linearly with the number of hits, it leads to ever-increasing demand for CPU, as the LHC will continue to increase the beam intensities over the next decades. This motivates investigating novel, non-combinatorial approaches for track finding, which could lead to huge savings in CPU needs over the LHC lifetime. The aim of this project is to explore and compare pattern recognition methods based on integral transformations, in particular, iterative re-weighted Hough Transform (HT) and multiscale Radon Transform (RT) with a set of predefined hit patterns. Machine learning (ML) is a crucial component in both approaches. For example, a ML-based classifier can analyse feature vectors extracted from HT images in order to detect hits from tracks of interest. For the RT approach, pattern set optimization is crucial for performance and overall feasibility, especially, for hardware-based track finding. Using the ML techniques, the pattern set can be learned as an approximation of sparsely coded set of training reference tracks representing expected events of interest.