Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data
This improves vertex reconstruction for high-energy physics experiments like MINERvA, though it appears incremental.
The paper tackles vertex reconstruction of neutrino-nucleus interaction events by combining energy and timing data with a deep learning approach, achieving 4.00% higher classification accuracy and a coefficient of determination of 0.9919 compared to previous methods.
We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine both energy and timing data that are collected in the MINERvA detector to perform classification and regression tasks. We show that the resulting network achieves higher accuracy than previous 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% on classification accuracy. For the regression task, our model achieves 0.9919 on the coefficient of determination, higher than the previous work (0.96).