2019/10/29 by L. Meduna, B. Bergmann, Meduna, Lukáš +9
Computer Science · Physics and Astronomy · #Distributed and Parallel Computing Systems #FOS: Physical sciences #Instrumentation and Detectors (physics.ins-det) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies #Radiation Detection and Scintillator Technologies
paper · pdf · doi:10.48550/arxiv.1910.13356
openalex publication_date 2019/10/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the next-generation Timepix3 hybrid pixel detector, new possibilities and challenges have arisen. The Timepix3 segments active sensor area of 1.98 cm2 into a square matrix of 256 x 256 pixels. In each pixel, the Time of Arrival (ToA, with a time binning of 1.56 ns) and Time over Threshold (ToT, energy) are measured simultaneously in a data-driven, i.e. self-triggered, read-out scheme. This contribution presents a framework for data acquisition, real-time clustering, visualization, classification and data saving. All of these tasks can be performed online, directly from multiple readouts through UDP protocol. Clusters are reconstructed on a pixel-by-pixel decision from the stream of not-necessarily chronologically sorted pixel data. To achieve quick spatial pixel-to-cluster matching, non-trivial data structures (quadtree) are utilized. Furthermore, parallelism (i.e multi-threaded architecture) is used to further improve the performance of the framework. Such real-time clustering offers the advantages of online filtering and classification of events. Versatility of the software is ensured by supporting all major operating systems (macOS, Windows and Linux) with both graphical and command-line interfaces. The performance of the real-time clustering and applied filtration methods are demonstrated using data from the Timepix3 network installed in the ATLAS and MoEDAL experiments at CERN.