Reconstruction of the event vertex in the PandaX-III experiment with convolution neural network

TL;DR

This paper introduces a convolution neural network approach to accurately reconstruct the event vertex in the PandaX-III experiment, significantly improving energy resolution and demonstrating effectiveness on both simulated and experimental data.

Contribution

The paper presents VGGZ0net, a CNN model for vertex reconstruction in TPCs, achieving high precision and improving detector energy resolution.

Findings

Achieves 11 cm precision in vertex position reconstruction.

Improves energy resolution from 10.1% to 4.0% FWHM after correction.

Successfully applies CNN to both simulated and real experimental data.

Abstract

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 $z_0$, the vertex position in drift direction at which the event takes place. In this paper, we propose to reconstruct $z_0$ 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 $z_0$ 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 $^{136}$Xe 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 $z_0$ reconstruction.