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

Abstract

Abstract The PandaX-III experiment uses a high-pressure xenon gaseous time projection chamber (TPC) to search for the neutrinoless double beta decay (0νββ) of 136Xe. The absence of the vertex position in the electron drift direction at which the event takes place in the detector limits the PandaX-III TPC’s performance. The charged particle tracks recorded by the TPC provide a possibility for vertex reconstruction. In this paper, a convolution neural network (CNN) model VGGZ0net is proposed for the reconstruction of vertex position. An 11 cm precision is achieved with the Monte Carlo simulation events uniformly distributed along a maximum drift distance of 120 cm. The electron loss during the drift under the different gas conditions is studied, and after the distance-based correction, the detector energy resolution is significantly improved. The CNN model is also verified successfully using the experimental data of the PandaX-III prototype detector.

Keywords

• Dark Matter and Double Beta Decay (experiments)