A search for two-component Majorana dark matter in a simplified model using the full exposure data of PandaX-II experiment
High Energy Physics - Experiment
2022-06-29 v1 High Energy Physics - Phenomenology
Abstract
In the two-component Majorana dark matter model, one dark matter particle can scatter off the target nuclei, and turn into a slightly heavier component. In the framework of a simplified model with a vector boson mediator, both the tree-level and loop-level processes contribute to the signal in direct detection experiment. In this paper, we report the search results for such dark matter from PandaX-II experiment, using total data of the full 100.7 tonneday exposure. No significant excess is observed, so strong constraints on the combined parameter space of mediator mass and dark matter mass are derived. With the complementary search results from collider experiments, a large range of parameter space can be excluded.
Keywords
Cite
@article{arxiv.2205.08066,
title = {A search for two-component Majorana dark matter in a simplified model using the full exposure data of PandaX-II experiment},
author = {Ying Yuan and Abdusalam Abdukerim and Zihao Bo and Wei Chen and Xun Chen and Yunhua Chen and Chen Cheng and Xiangyi Cui and Yingjie Fan and Deqing Fang and Changbo Fu and Mengting Fu and Lisheng Geng and Karl Giboni and Linhui Gu and Xuyuan Guo and Ke Han and Changda He and Jinrong He and Di Huang and Yanlin Huang and Zhou Huang and Ruquan Hou and Xiangdong Ji and Yonglin Ju and Chenxiang Li and Mingchuan Li and Shu Li and Shuaijie Li and Qing Lin and Jianglai Liu and Xiaoying Lu and Lingyin Luo and Wenbo Ma and Yugang Ma and Yajun Mao and Yue Meng and Nasir Shaheed and Xuyang Ning and Ningchun Qi and Zhicheng Qian and Xiangxiang Ren and Changsong Shang and Guofang Shen and Lin Si and Wenliang Sun and Andi Tan and Yi Tao and Anqing Wang and Meng Wang and Qiuhong Wang and Shaobo Wang and Siguang Wang and Wei Wang and Xiuli Wang and Zhou Wang and Mengmeng Wu and Weihao Wu and Jingkai Xia and Mengjiao Xiao and Xiang Xiao and Pengwei Xie and Binbin Yan and Xiyu Yan and Jijun Yang and Yong Yang and Chunxu Yu and Jumin Yuan and Dan Zhang and Minzhen Zhang and Peng Zhang and Tao Zhang and Li Zhao and Qibin Zheng and Jifang Zhou and Ning Zhou and Xiaopeng Zhou and Yong Zhou},
journal= {arXiv preprint arXiv:2205.08066},
year = {2022}
}