Boosted Higgs boson jet reconstruction via a graph neural network
文献类型：期刊论文
| 作者 | Guo, Jun; Li, Jinmian1; Li, Tianjun2,3 ; Zhang, Rao1
|
| 刊名 | PHYSICAL REVIEW D
 |
| 出版日期 | 2021 |
| 卷号 | 103期号:11页码:116025 |
| ISSN号 | 2470-0010 |
| DOI | 10.1103/PhysRevD.103.116025 |
| 英文摘要 | By representing each collider event as a point cloud, we adopt the graphic convolutional network (GCN) with focal loss to reconstruct the Higgs jet in it. This method provides higher Higgs tagging efficiency and better reconstruction accuracy than the traditional methods, which use jet substructure information. The GCN, which is trained on events of the H + jets process, is capable of detecting a Higgs jet in events of several different processes, even though the performance degrades when there are boosted heavy particles other than the Higgs boson in the event. We also demonstrate the signal and background discrimination capacity of the GCN by applying it to the t (t) over bar process. Taking the outputs of the network as new features to complement the traditional jet substructure variables, the t (t) over bar events can be separated further from the H + jets events. |
| 学科主题 | Astronomy & Astrophysics ; Physics |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/27279]  |
| 专题 | 理论物理研究所_理论物理所1978-2010年知识产出 |
| 作者单位 | 1.Jiangxi Normal Univ, Dept Phys, Nanchang 330022, Jiangxi, Peoples R China 2.Sichuan Univ, Coll Phys, Chengdu 610065, Peoples R China 3.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Phys Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Jun,Li, Jinmian,Li, Tianjun,et al. Boosted Higgs boson jet reconstruction via a graph neural network[J]. PHYSICAL REVIEW D,2021,103(11):116025. |
| APA | Guo, Jun,Li, Jinmian,Li, Tianjun,&Zhang, Rao.(2021).Boosted Higgs boson jet reconstruction via a graph neural network.PHYSICAL REVIEW D,103(11),116025. |
| MLA | Guo, Jun,et al."Boosted Higgs boson jet reconstruction via a graph neural network".PHYSICAL REVIEW D 103.11(2021):116025. |
入库方式： OAI收割
来源：理论物理研究所
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