Machine learning methods are being introduced at all stages of data
reconstruction and analysis in various high-energy physics experiments. We
present the development and application of convolutional neural networks with
modified autoencoder architecture for the reconstruction of the pulse arrival
time and amplitude in individual scintillating crystals in electromagnetic
calorimeters and other detectors. The network performance is discussed as well
as the application of xAI methods for further investigation of the algorithm
and improvement of the output accuracy.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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