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Fast timing detector is widely used for its picosecond (ps)-level timing resolution, such as Photek-PMT210 with about 50 ps at single-photon electron (SPE) mode. The time characteristics of these types of fast timing photodetectors include rise time, fall time, and electron transit time (TT) of the output signal. The test results of fast timing detector are affected by the light 7 source, the electronic device board, the data acquisition (DAQ) system, and so on. In this article, some comparative test results will be shown and discussed on the factors affecting the performance of fast timing detector. [ABSTRACT FROM AUTHOR]

The photomultiplier tube (PMT) and silicon photomultiplier (SiPM) of small areas have the characteristics of fast time response, suitable for the detection of fast and extremely weak light signals, and the transit time spread (TTS) can be on the picosecond scale. The test system is generally divided into two types: one is the direct acquisition of the waveform (DAW), (DAW such as the high-sampling oscilloscope), and the other is an indirect acquisition of the waveform (IDAW). In this article, both DAW and IDAW will be used to carry out the comparative test of ultra-fast photodetectors to complete the calibration of the performance of ultra-fast detectors and the study on time test systems. This will be helpful for beam experiments to be carried out in the laboratory and provide effective support for the development of domestic fast PMT. [ABSTRACT FROM AUTHOR]

The upgrade of the BESIII end cap time-of-flight (TOF) detector needs to achieve a ~40-ps time resolution level to provide high-performance reference time for beam testing. Fast silicon photomultiplier tubes (SiPMs) have excellent time characteristics and the transition time spread (TTS) can reach tens of picoseconds. Therefore, SiPMs are selected to replace the PMTs in T0 detectors. In this article, we describe that the time characteristics of a fast SiPM and T0 system were studied using a high-speed real-time oscilloscope. In order to obtain the optimal time resolution, coupling techniques of the plastic scintillators and SiPMs were studied. Single-ended coupling between a SiPM and a plastic scintillator can obtain the best time resolution. [ABSTRACT FROM AUTHOR]

To realize neutron-gamma discrimination of piled-up pulses in high counting rate circumstances, the discrimination performance of the charge comparison method and three neural network models to actual piled-up pulses was studied in this paper. The neural network models, including a residual neural network (ResNet), a convolutional neural network (CNN) and a fully connected neural network (FCNN), were trained and tested by seven kinds of actual experimental data sets, which included the background signals (bg), non-piled-up neutron (n) and gamma (g) pulses as well as piled-up neutron + neutron (n + n), neutron + gamma (n + g), gamma + neutron (g + n), and gamma + gamma (g + g) pulses. The labels of the seven data sets were provided after discriminating by the charge comparison method. The results showed that the charge comparison method could be applied to discriminate piled-up neutron-gamma pulses with the Figure of Merit (FoM) value of 1.0. The integration length has been optimized to be different for piled-up and non-piled-up pulses, but follow the same criterion for neutron and gamma discrimination. The FoM values are worse than that of non-piled-up signals because the baseline fluctuation is very large under high counting rate conditions, which gives lots of interference to pulse shape discrimination. The ResNet model has the highest total prediction accuracy (93.85%) for the seven signal types, and through the comparison and analysis of the inconsistent events discriminated by the charge comparison method and the ResNet model, it is concluded that the discrimination result of the ResNet model is more accurate. These results indicate that both the charge comparison method and residual neural network can be used for complicated n/ γ discrimination under high counting rate conditions, and the residual neural network works better. [ABSTRACT FROM AUTHOR]