This paper describes a recently developed digital-based data acquisition system for electrical capacitance tomography (ECT). The system consists of high-capacity field-programmable gate arrays (FPGA) and fast data conversion circuits together with a specific signal processing method. In this system, digital phase-sensitive demodulation is implemented. A specific data acquisition scheme is employed to deal with residual charges in each measurement, resulting in a high signal-to-noise ratio (SNR) at high excitation frequency. A high-speed USB interface is employed between the FPGA and a host PC. Software in Visual C++ has been developed to accomplish operational functions. Various tests were performed to evaluate the system, e.g. frame rate, SNR, noise level, linearity, and static and dynamic imaging. The SNR is 60.3 dB at 1542 frames s−1 for a 12-electrode sensor. The mean absolute error between the measured capacitance and the linear fit value is 1.6 fF. The standard deviation of the measurements is in the order of 0.1 fF. The dynamic imaging test demonstrates the advantages of high temporal resolution of the system. The experimental results indicate that the digital signal processing devices can be used to construct a high-performance ECT system.
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.
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