arXiv subjects

Stanislav Pospíšil

Publications and source records attributed to Stanislav Pospíšil.

4 recordsLinked to original sources

arXiv2026

Analysis of mixed radiation fields at the MoEDAL experiment based on real-time data from a Timepix detector network

The primary objective of this work is the determination of fluences and characteristics of fast neutrons, other hadrons, and highly ionizing particles (HIPs) in the environment of the MoEDAL experiment at the Large Hadron Collider. These particles may constitute an experimental background for the passive Nuclear Track Detectors (NTDs) used by MoEDAL to search for tracks potentially produced by magnetic monopoles, in particular by particles indistinguishable in NTD from monopoles. The study is based on data acquired by the Timepix hybrid silicon pixel detector network, which represents the first and only active detector system installed and operated as part of the MoEDAL experiment from 2013 to 2018. The Timepix detector network enables real-time measurements of mixed radiation fields, including the composition, spectral properties, and directional characteristics of individual radiation components across different regions of the MoEDAL experimental area. The paper presents detailed results of the radiation field analysis with emphasis on neutrons and HIPs, including their directional distributions. The first results demonstrating the spatial tracking capabilities of the Timepix detectors are also reported, illustrating the reconstruction of particle direction and energy-loss profiles from individual detector frames.

physics.ins-det↗

arXiv2022

Improved algorithms for determination of particle directions with Timepix3

Timepix3 pixel detectors have demonstrated great potential for tracking applications. With $256\times 256$ pixels, 55 $\mathrmμ$m pitch and improved resolution in time (1.56 ns) and energy (2 keV at 60 keV), they have become powerful instruments for characterization of unknown radiation fields. A crucial pre-processing step for such analysis is the determination of particle trajectories in 3D space from individual tracks. This study presents a comprehensive comparison of regression methods that tackle this task under the assumption of track linearity. The proposed methods were first evaluated on a simulation and assessed by their accuracy and computational time. Selected methods were then validated with a real-world dataset, which was measured in a well-known radiation field. Finally, the presented methods were applied to experimental data from the Large Hadron Collider. The best-performing methods achieved a mean absolute error of 1.99° and 3.90° in incidence angle $θ$ and azimuth $φ$, respectively. The fastest presented method required a mean computational time of 0.02 ps per track. For all experimental applications, we present angular maps and stopping power spectra.

physics.data-an↗

arXiv2019

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

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.

physics.data-an↗

arXiv2019

Real-time Timepix3 data clustering, visualization and classification with a new Clusterer framework

With the next-generation Timepix3 hybrid pixel detector, new possibilities and challenges have arisen. The Timepix3 segments active sensor area of 1.98 $cm^2$ 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.

physics.ins-det↗