PMT Waveform Timing Analysis Using Machine Learning Method

The timing information carried by the photomultiplier tube (PMT) waveform is important when reconstructing the particle trajectory, especially in the application of time-of-flight detectors. The traditionally used timing methods, including leading edge discrimination (LED) and constant fraction discrimination (CFD), are easily realized in the circuit. Since the timing point of the signal should be determined by the whole signal waveform, the CFD and LED method cannot reach the best time resolution. With the development of a fast analog-to-digital converter (FADC) which makes the whole waveform available, more intelligent methods are developed to realize more precise timing. Former studies show that PMT-waveform-analysis model based on convolutional neural network (CNN) can improve the coincidence time resolution (CTR) of a pair of PMTs by nearly 20%, but the limit of number of labels directly affects the CNN timing performance. In this article, a new method based on the CNN model is introduced for the timing of a pair of Cherenkov-detection micro-channel-plate PMTs (MCP-PMTs). The results show a great improvement compared with the CFD method.