Using Deep Neural Networks to Improve the Precision of Fast-Sampled Particle Timing Detectors


  • Mateusz Kocot AGH University of Krakow
  • Krzysztof Misan AGH University of Krakow
  • Leszek Grzanka AGH University of Krakow
  • Valentina Avati AGH University of Krakow
  • Edoardo Bossini INFN Sezione di Pisa, Pisa, Italy
  • Nicola Minafra Department of Physics and Astronomy, University of Kansas, Lawrence, KS, United States



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.


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How to Cite

Kocot, M., Misan, K., Grzanka, L., Avati, V., Bossini, E., & Minafra, N. (2024). Using Deep Neural Networks to Improve the Precision of Fast-Sampled Particle Timing Detectors. Computer Science, 25(1).