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About this paper
Sichen Li, Mélissa Zacharias, Jochem Snuverink, Jaime Coello de Portugal, Fernando Perez-Cruz, Davide Reggiani, Andreas Adelmann
The RPCNN classifies accelerator sensor windows to identify patterns preceding beam interlocks. It converts short histories of 97-channel readings into recurrence-plot images and uses a convolutional neural network. On time-ordered validation, its best run reached 0.71 AUC, versus 0.65 for the best random forest, but estimated beam-time savings favored the forest; the mitigation remains hypothetical.
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