Study of background from accidental coincidence signals in the PandaX-II experiment

TL;DR

This paper investigates the accidental coincidence background in the PandaX-II dark matter detector, identifying its origins and applying machine learning to significantly reduce background noise, thereby enhancing the experiment's sensitivity.

Contribution

It provides a detailed analysis of accidental coincidence backgrounds and introduces a boosted-decision-tree method to suppress this background in PandaX-II.

Findings

Accidental coincidence background reduced by 70% using machine learning.

Enhanced sensitivity in dark matter search due to background suppression.

Identified sources and characteristics of isolated signals in the detector.

Abstract

The PandaX-II experiment employed a 580kg liquid xenon detector to search for the interactions between dark matter particles and the target xenon atoms. The accidental coincidences of isolated signals result in a dangerous background which mimic the signature of the dark matter. We performed a detailed study on the accidental coincidence background in PandaX-II, including the possible origin of the isolated signals, the background level and corresponding background suppression method. With a boosted-decision-tree algorithm, the accidental coincidence background is reduced by 70% in the dark matter signal region, thus the sensitivity of dark matter search at PandaX-II is improved.