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

## TL;DR

This paper introduces a deep learning model that combines energy and timing data for improved vertex reconstruction in neutrino-nucleus interactions, outperforming previous methods in accuracy and efficiency.

## Contribution

The paper presents a novel deep learning approach that integrates energy and time data for vertex reconstruction, achieving higher accuracy with a smaller model and less training time.

## Key findings

- 4.00% improvement in classification accuracy
- 0.9919 coefficient of determination in regression
- Smaller model size and reduced training time

## 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 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).