Chapter 3 Extra-galactic Gamma-Ray Sources

Extra-galactic gamma-ray sources, such as gamma-ray bursts, active galactic nuclei, starburst galaxies, are interesting and important targets for LHAASO observations. In this chapter, the prospects of detecting these sources with LHAASO and their physical implications are studied. The upgrade plan for the Water Cherenkov Detector Array (WCDA), which aims to enhance the detectability of relatively lower energy photons, is also presented. In addition, a study on constraining the extragalactic background light with LHAASO observation of blazars is presented.

Chapter 1 LHAASO Instruments and Detector technology

The Large High Altitude Air Shower Observatory ( LHAASO ) ( Fig. 1 ) is located at Mt. Haizi (4410 m a.s.l., 600 g/cm2, 29◦ 21’ 27.56” N, 100◦ 08’ 19.66” E) in Daocheng, Sichuan province, P.R. China. LHAASO consists of 1.3 km2 array ( KM2A ) of electromagnetic particle detectors ( ED ) and muon detectors ( MD ), a water Cherenkov detector array ( WCDA ) with a total active area of 78,000 m2, 18 wide field-of-view air Cherenkov telescopes (WFCTA ) and a newly proposed electron-neutron detector array ( ENDA ) covering 10,000 m2. Each detector is synchronized with all the other through a clock synchronization network based on the White Rabbit protocol. The observatory includes an IT center which comprises the data acquisition system and trigger system, the data analysis facility. In the following of this Chapter, all the above mentioned components of LHAASO will be briefly described, together with infrastructure which is a fundamental component of the LHAASO observatory.

Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using machine learning techniques

The muon tagging is an essential tool to distinguish between gamma and hadron-induced showers in wide field-of-view gamma-ray observatories. In this work, it is shown that an efficient muon tagging (and counting) can be achieved using a water Cherenkov detector with a reduced water volume and 4 PMTs, provided that the PMT signal spatial and time patterns are interpreted by an analysis based on machine learning (ML). The developed analysis has been tested for different shower and array configurations. The output of the ML analysis, the probability of having a muon in the WCD station, has been used to notably discriminate between gamma and hadron induced showers with $$S/ \sqrt{B} \sim 4$$ S / B ∼ 4 for shower with energies $$E_0 \sim 1\,$$ E 0 ∼ 1 TeV. Finally, for proton-induced showers, an estimator of the number of muons was built by means of the sum of the probabilities of having a muon in the stations. Resolutions about $$20\%$$ 20 % and a negligible bias are obtained for vertical showers with $$N_{\mu } > 10$$ N μ > 10 .

Muon–Electron Pulse Shape Discrimination for Water Cherenkov Detectors Based on FPGA/SoC

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.