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1 2024

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Using deep neural networksto improve the precision of fast-sampled particle timing detectors

Kocot Mateusz, Misan Krzysztof, Avati Valentina, Bossini Edoardo, Grzanka Leszek, Minafra Nicola

Computer Science

2024

T. 25 (1)

43--61

Measurements from particle timing detectors are often affected by the timewalk effect caused by statistical fluctuations in the charge deposited by passingparticles. The constant fraction discriminator (CFD) algorithm is frequentlyused 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 andeffective but does not leverage all voltage samples in a time series. Its performance could be enhanced with deep neural networks, which are commonlyused for time series analysis, including computing the particle arrival time. Weevaluated various neural network architectures using data acquired at the testbeam facility in the DESY-II synchrotron, where a precise MCP (MicroChan-nel Plate) detector was installed in addition to PPS diamond timing detectors.MCP measurements were used as a reference to train the networks and com-pare the results with the standard CFD method. Ultimately, we improved thetiming precision by 8% to 23%, depending on the detector’s readout channel.The best results were obtained using a UNet-based model, which outperformedclassical convolutional networks and the multilayer perceptron.