Suppression of accidental backgrounds with deep neural networks in the PandaX-II experiment

作     者：Shaheed, Nasir Chen, Xun Wang, Meng 

作者机构：Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China INPAC School of Physics and Astronomy Shanghai Jiao Tong University MOE Key Lab for Particle Physics Astrophysics and Cosmology Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Deep neural networks 

摘      要：The PandaX dark matter detection project searches for dark matter particles using the technology of dual phase xenon time projection chamber. The low expected rate of the signal events makes the control of backgrounds crucial for the experiment success. In addition to reducing external and internal backgrounds during the construction and operation of the detector, special techniques are employed to suppress the background events during the data analysis. In this article, we demonstrate the use of deep neural networks (DNNs) for suppressing the accidental backgrounds, as an alternative to the boosted-decision-tree method used in previous analysis of PandaX-II. A new data preparation approach is proposed to enhance the stability of the machine learning algorithms to be run and ultimately the sensitivity of the final data analysis. Copyright © 2023, The Authors. All rights reserved.