A scalable High Voltage Power Supply System with system on chip control for Micro Pattern Gaseous Detectors

The requirements posed to high voltage power supply systems by the operation of Micro Pattern Gaseous Detectors are specific in terms of high resolution diagnostic features and intelligent dynamic voltage control. These requirements are needed both when technology development is performed and when extended detector systems are supplied and monitored. Systems satisfying all the needed features are not commercially available. A single channel high voltage system matching the Micro Pattern Gaseous Detector needs has been designed and realized, including its hardware and software components. The system employs a commercial DCDC converter and is coupled to a custom high resolution ammeter. Local intelligence, flexibility and high speed inter-connectivity are provided by a System on Chip Board and the use of a powerful FPGA. The single channel system has been developed, as critical milestone towards the realization of a multi-channel system. The design, implementation and performance of the system are reported in detail in this article, as well as the performance of the single channel power supply when connected to a Micro Pattern Gaseous Detector in realistic working condition during a test beam exercise

Pulse Shape Discrimination for Online Data Acquisition in Water Cherenkov Detectors Based on FPGA/SoC

Discrimination of secondary particles produced in extensive air showers is needed to study the composition of primary cosmic rays. High speed data acquisition and the increase in resources in modern FPGAs with the addition of a microprocessor in System-on-Chip (SoC) technologies allow to implement complex algorithms for digital signal analysis. Pulse shape Discrimination (PSD) can be carried out in real-time on the digital front-end of the detector; indeed online data analysis permits to save computational resources in post-processing and transmission bandwidth. We describe two methods for PSD, the first one based on artificial neural network (ANN) using the novel hls4ml package, and the other based on a correlation approach using finite impulse response (FIR) filters. Both methods were implemented and tested on Xilinx FPGA SoC devices ZU9EG Zynq Ultrascale+ and XC7Z020 Zynq. Data from a Water Cherenkov Detector (WCD) were acquired with a 500 Mhz, 8-bit high speed analog-to-digital converter acquisition system. Experimental results obtained with both methods are presented along with timing, accuracy and resources utilization analysis