Randomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 Detectors
This addresses real-time pattern recognition for particle physics experiments, offering incremental improvements in efficiency and dimensionality handling.
The paper tackled the problem of efficiently analyzing ionizing particle clusters in Timepix and Timepix3 detectors by introducing randomized pattern recognition algorithms, achieving correct separation of overlapping tracks in Timepix and enabling 3D reconstruction in Timepix3 with probabilistic accuracy bounds.
Timepix and Timepix3 are hybrid pixel detectors ($256\times 256$ pixels), capable of tracking ionizing particles as isolated clusters of pixels. To efficiently analyze such clusters at potentially high rates, we introduce multiple randomized pattern recognition algorithms inspired by computer vision. Offering desirable probabilistic bounds on accuracy and complexity, the presented methods are well-suited for use in real-time applications, and some may even be modified to tackle trans-dimensional problems. In Timepix detectors, which do not support data-driven acquisition, they have been shown to correctly separate clusters of overlapping tracks. In Timepix3 detectors, simultaneous acquisition of Time-of-Arrival (ToA) and Time-over-Threshold (ToT) pixel data enables reconstruction of the depth, transitioning from 2D to 3D point clouds. The presented algorithms have been tested on simulated inputs, test beam data from the Heidelberg Ion therapy Center and the Super Proton Synchrotron and were applied to data acquired in the MoEDAL and ATLAS experiments at CERN.