LAGO is an extended cosmic ray observatory composed of water-Cherenkov detectors (WCD) placed throughout Latin America. It is dedicated to the study of various issues related to astrophysics, space weather and atmospheric physics at the regional scale. In this paper we present the design and implementation of the front-end electronics and the data acquisition system for readout of the WCDs of LAGO. The system consists of preamplifiers and a digital board sending data to a computer via an USB interface. The analog signals are acquired from three independent channels at a maximum rate of $ 1.2 Â 10 5 pulses per second and a sampling rate of 40 MHz. To avoid false trigger due to baseline fluctuations, we present in this work a baseline correction algorithm that makes it possible to use WCDs to study variations of the environmental radiation. A data logging software has been designed to format the received data. It also enables an easy access to the data for an off-line analysis, together with the operational conditions and environmental information. The system is currently used at different sites of LAGO.
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.
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