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We present a deep learning approach for vertex reconstruction ofneutrino-nucleus interaction events, a problem in the domain of high energyphysics. In this approach, we combine both energy and timing data that arecollected in the MINERvA detector to perform classification and regressiontasks. We show that the resulting network achieves higher accuracy thanprevious results while requiring a smaller model size and less training time.In particular, the proposed model outperforms the state-of-the-art by 4.00% onclassification accuracy. For the regression task, our model achieves 0.9919 onthe coefficient of determination, higher than the previous work (0.96).

Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data