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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21670%2F19%3A00349557" target="_blank" >RIV/68407700:21670/19:00349557 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.48550/arXiv.1910.13356" target="_blank" >https://doi.org/10.48550/arXiv.1910.13356</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.48550/arXiv.1910.13356" target="_blank" >10.48550/arXiv.1910.13356</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 256x256 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 256x256 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.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

21100 - Other engineering and technologies

Projekt

<a href="/cs/project/EF16_013%2F0001785" target="_blank" >EF16_013/0001785: Urychlovač Van de Graaff - laditelný zdroj monoenergetických neutronů a lehkých iontů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů