Ústav technické a experimentální fyziky Institute of Experimental and Applied Physics

Randomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 Detectors

NázevTitle
Randomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 DetectorsRandomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 Detectors
Druh výsledkuResult type
Příspěvek ve sborníkuProceedings paper
AutořiAuthors
P. Mánek, B. Bergmann, P. Burian, L. Meduna, S. Pospíšil, M. Suk
DOIDOI
10.48550/arXiv.1911.02367
Časopis / citaceJournal / citation
In: Proceedings of Connecting the Dots and Workshop on Intelligent Trackers 2019. ArXiv, 2019.
JazykLanguage
eng
RIVRIV
RIV/68407700:21670/19:00331872!RIV22-MSM-21670___
ProjektProject
CERN-CZ - Výzkumná infrastruktura pro experimenty v CERN - LM2015058 (2016–2019)CERN-CZ - Výzkumná infrastruktura pro experimenty v CERN - LM2015058 (2016–2019); Urychlovač Van de Graaff - laditelný zdroj monoenergetických neutronů a lehkých iontůVan de Graaff Accelerator - a Tunable Source of Monoenergetic Neutrons and Light Ions

AbstraktAbstract

Timepix and Timepix3 are hybrid pixel detectors (256×256 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.

Timepix and Timepix3 are hybrid pixel detectors (256×256 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.