The tracks left by charged particles in a gaseous time projection chamber~(TPC) incorporate important information about the interaction process and drift of electrons in gas. The electron diffusion information carried by the tracks is an effective signature to reconstruct z0, the vertex position in drift direction at which the event takes place. In this paper, we propose to reconstruct z0 with convolution neural network~(CNN) in the PandaX-III experiment. A CNN model VGGZ0net is built and validated with Monte Carlo simulation data. It gives z0 with a 11~cm precision for the events above 2~MeV uniformly distributed along a drift distance of 120~cm, and then the electron lifetime can be deduced. The energy resolution of detector is significantly improved after the electron lifetime correction, i.e., from 10.1\% to 4.0\% FWHM at the Q-value of double beta decay of 136Xe for the scenario with an electron lifetime of 6.5~ms. The CNN model is also successfully applied to the experimental data of the PandaX-III prototype detector for z0 reconstruction.
@article{arxiv.2211.14992,
title = {Reconstruction of the event vertex in the PandaX-III experiment with convolution neural network},
author = {Tao Li and Yu Chen and Shaobo Wang and Ke Han and Heng Lin and Kaixiang Ni and Wei Wang},
journal= {arXiv preprint arXiv:2211.14992},
year = {2023}
}