Position reconstruction and surface background model for the PandaX-4T detector

TL;DR

This paper presents advanced position reconstruction algorithms and a surface background model for the PandaX-4T dark matter detector, improving event localization and background estimation accuracy.

Contribution

It introduces two novel position reconstruction algorithms, selects the optimal one based on comprehensive evaluation, and develops a data-driven surface background model for PandaX-4T.

Findings

PAF method achieves 1.0 mm bulk event resolution

Surface event resolution is 4.4 mm

Surface background estimated at 0.09 and 0.17 events for two runs

Abstract

We report the position reconstruction methods and surface background model for the PandaX-4T dark matter direct search experiment. This work develops two position reconstruction algorithms: template matching (TM) method and photon acceptance function (PAF) method. Both methods determine the horizontal position of events based on the light pattern of secondary scintillation collected by the light sensors. After a comprehensive evaluation of resolution, uniformity, and robustness, the PAF method was selected for position reconstruction, while the TM method was employed for verification. The PAF method achieves a bulk event resolution of 1.0 mm and a surface event resolution of 4.4 mm for a typical $S2$ signal with a bottom charge of 1500 PE (about 14 keV). The uniformity is around 20\%. Robustness studies reveal average deviations of 5.1 mm and 8.8 mm for the commissioning run (Run0) and the first science run (Run1), respectively, due to the deactivation of certain PMTs. A data-driven surface background model is developed based on the PAF method. The surface background is estimated to be $0.09 \pm 0.06$ events for Run0 (0.54 tonne$\cdot$year) and $0.17 \pm 0.11$ events for Run1 (1.00 tonne$\cdot$year).