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A simplified correlation index is proposed to be used in real-time pulse shape recognition systems. This index is similar to the classic Pearson’s correlation coefficient, but it can be efficiently implemented in FPGA devices with far fewer logic resources and excellent performance. Numerical simulations with synthetic data and comparisons with the Pearson’s correlation show the suitability of the proposed index in applications such as the discrimination and counting of pulses with a predefined shape. Superior performance is evident in signal-to-noise ratio scenarios close to unity. FPGA implementation of Person’s method and the proposed correlation index have been successfully tested and the main results are summarized.

The distinction of secondary particles in extensive air showers, specifically muons and electrons, is one of the requirements to perform a good measurement of the composition of primary cosmic rays. We describe two methods for pulse shape detection and discrimination of muons and electrons implemented on FPGA. One uses an artificial neural network (ANN) algorithm, the other exploits a correlation approach based on finite impulse response (FIR) filters. The novel hls4ml package is used to build the ANN inference model. Both methods were implemented and tested on Xilinx FPGA System on Chip (SoC) devices: ZU9EG Zynq UltraScale+ and ZC7Z020 Zynq. The data set used for the analysis was captured with a data acquisition system on an experimental site based on a water Cherenkov detector. A comparison of the accuracy of the detection, resources utilization and power consumption of both methods is presented. The results show an overall accuracy on particle discrimination of 96.62% for the ANN and 92.50% for the FIR-based correlation, with execution times of 848 ns and 752 ns, respectively.

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.

In this study, we present a procedure to optimize a set of finite impulse response filter (FIR) coefficients for digital pulse-amplitude measurement. Such an optimized filter is designed using an adapted digital penalized least mean square (DPLMS) method. The effectiveness of the procedure is demonstrated using a dataset from a case study on high-resolution X-ray spectroscopy based on singlephoton detection and energy measurements. The energy resolutions of the Kα and Kβ lines of the Manganese energy spectrum have been improved by approximately 20%, compared to the reference values obtained by fitting individual photon pulses with the corresponding mathematical model. Fil: Mannatunga, Kasun Sameera. International Centre for Theoretical Physics; Italia Fil: Mannatunga, Kasun Sameera. University of Sri Jayewardenepura; Sri Lanka Fil: Mannatunga, Kasun Sameera. University of Moratuwa; Sri Lanka Fil: Valinoti, Bruno. Instituto Nacional de Tecnología Industrial; Argentina Fil: Valinoti, Bruno. International Centre for Theoretical Physics; Italia Fil: Valinoti, Bruno. Università degli Studi di Trieste; Italia Fil: Samayoa, Werner Florian. International Centre for Theoretical Physics; Italia Fil: Samayoa, Werner Florian. Università degli Studi di Trieste; Italia Fil: Crespo, Maria Liz. International Centre for Theoretical Physics; Italia Fil: Cicuttin, Andres. International Centre for Theoretical Physics; Italia Fil: Kamdem, Jerome Folla. International Centre for Theoretical Physics; Italia Fil: Kamdem, Jerome Folla. University of Yaoundé I; Camerún Fil: Garcia, Luis Guillermo. International Centre for Theoretical Physics; Italia Fil: Garcia, Luis Guillermo. Università degli Studi di Trieste; Italia Fil: Carrato, Sergio. University of Yaoundé I; Camerún

A portable architectural design strategy is described for the implementation of reconfigurable virtual instrumentation based on programmable Systems-on-Chip integrating microprocessors and FPGA in the same physical device. The key role is played by a general purpose communication block as a means to efficiently separate the activities carried out in the microprocessor and in the FPGA. Both parts interact according to simple logic protocols by reading and writing data on the common memory resources of the communication block. The architecture of the proposed communication system can be easily implemented in practically any modern programmable System-on-Chip. With the proposed strategy, the porting of embedded software programs and associated FPGA designs among different device families and vendors is facilitated. A structured methodology is proposed for handling complex real-time systems based on these programmable Systems-on-Chip. We described a concrete communication block that has been successfully implemented and utilized for a quick implementation of a data acquisition system based on a Xilinx Zynq-7030 FPGA Mezzanine Card (FMC) and a custom FMC module with an 8-bit 500 MSPS ADC.

In spite of the constant technological improvements in the field of detector development, X-ray fluorescence (XRF) in the soft X-ray regime remains a challenge. The low intrinsic fluorescence yield for energies below 2 keV indeed renders the applicability of low-energy XRF still difficult. Here, we report on a new multi-element multi-tile detection system currently under development, designed to be integrated into a soft X-ray microscopy end station. The system will be installed at the TwinMic beamline of Elettra synchrotron (Trieste, Italy) in order to increase the detected count rate by up to an order of magnitude. The new architecture is very versatile and can be adapted to any XRF experimental setup. Even though the first results of the previous version of such a multi-element system were encouraging, several issues still needed to be addressed. The system described here represents a further step in the detector evolution. It is based on four trapezoidal-shaped monolithic silicon drift detector tiles (matrices) with six hexagonal elements each equipped with a custom ultra-low noise application-specific integrated circuit readout. The whole signal processing chain has been improved leading to an overall increase in performances, namely, in terms of energy resolution and acquisition rates. The design and development of this new detection system will be described, and recent results obtained at the TwinMic beamline at Elettra will be presented. Future perspectives and improvements will also be discussed.