Measurements from particle timing detectors are often affected by the time
walk effect caused by statistical fluctuations in the charge deposited by
passing particles. The constant fraction discriminator (CFD) algorithm is
frequently used to mitigate this effect both in test setups and in running
experiments, such as the CMS-PPS system at the CERN's LHC. The CFD is simple
and effective but does not leverage all voltage samples in a time series. Its
performance could be enhanced with deep neural networks, which are commonly
used for time series analysis, including computing the particle arrival time.
We evaluated various neural network architectures using data acquired at the
test beam facility in the DESY-II synchrotron, where a precise MCP
(MicroChannel Plate) detector was installed in addition to PPS diamond timing
detectors. MCP measurements were used as a reference to train the networks and
compare the results with the standard CFD method. Ultimately, we improved the
timing precision by 8% to 23%, depending on the detector's readout channel. The
best results were obtained using a UNet-based model, which outperformed
classical convolutional networks and the multilayer perceptron.

通过使用深度神经网络，该研究改进了粒子计时探测器的定时精度，从而减少了因时间走样效应引起的测量偏差，最终获得了 8% 到 23% 不等的定时精度提升。