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

Linghao Song; Fan Chen; Steven R. Young; Catherine D. Schuman; Gabriel N. Perdue; Thomas E. Potok

ICASSP 2019accepted0 citations

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

Linghao Song, Fan Chen, Steven R. Young, Catherine D. Schuman, Gabriel N. Perdue, Thomas E. Potok

Abstract

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 MIN-ERvA 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).

BibTeX

@inproceedings{icassp2019_deeplearningforv,
  title = {Deep Learning for Vertex Reconstruction of Neutrino-nucleus Interaction Events with Combined Energy and Time Data},
  author = {Linghao Song and Fan Chen and Steven R. Young and Catherine D. Schuman and Gabriel N. Perdue and Thomas E. Potok},
  booktitle = {ICASSP 2019},
  year = {2019}
}