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

TL;DR

This paper introduces randomized computer vision algorithms for pattern recognition in Timepix and Timepix3 detectors, enabling efficient, real-time analysis of ionizing particle tracks and 3D reconstruction.

Contribution

The paper presents novel probabilistic pattern recognition algorithms tailored for Timepix detectors, improving real-time cluster separation and 3D depth reconstruction capabilities.

Findings

Successfully separated overlapping clusters in Timepix detectors.

Reconstructed 3D particle tracks using combined ToA and ToT data in Timepix3.

Validated algorithms on simulated and real experimental data from CERN.

Abstract

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.