DAQ software for GEM-based imaging system
In the paper we report on the development of a dedicated data acquisition (DAQ) software being part of a full-filed X-ray fluorescence spectroscopy (XRF) imaging system based on a standard 10 cm × 10 cm 3-stage Gas Electron Multiplier (GEM) detector. Readout of a large area detector introduces addit...
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Veröffentlicht in: | Journal of instrumentation 2018-12, Vol.13 (12), p.C12016-C12016 |
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Sprache: | eng |
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Zusammenfassung: | In the paper we report on the development of a dedicated data acquisition (DAQ) software being part of a full-filed X-ray fluorescence spectroscopy (XRF) imaging system based on a standard 10 cm × 10 cm 3-stage Gas Electron Multiplier (GEM) detector. Readout of a large area detector introduces additional constraints and requirements for the front-end electronics, data acquisition system and the software which are necessary for efficient system operation. The details of the front-end electronics and the data acquisition system have already been reported elsewhere. Therefore, in the paper, we focus mostly on the software part of the developed system. The software suite consists of a few independent components, each responsible for the well-defined task. A crucial part of the software, in a form of a Graphical User Interface (GUI) application written in modern C++ language with help of Qt framework and Boost libraries, is responsible for system configuration, monitoring and online data reconstruction. During data-taking periods an automated system is utilized for batch recording of the measurement data, event reconstruction and processing with results visualization. This part of the software has been split into (a) dedicated raw data recorder, (b) non-GUI event reconstruction component (C++ based) able to use most of the workstation resources (mainly CPUs time) and (c) independent, flexible offline visualization component for plots or histograms preparation (Python based). All the components of the system are completely independent of each other. Therefore, distribution of the tasks to a few workstation PCs is straightforward and significantly accelerates processing. |
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ISSN: | 1748-0221 1748-0221 |
DOI: | 10.1088/1748-0221/13/12/C12016 |