期刊
JOURNAL OF HIGH ENERGY PHYSICS
卷 -, 期 10, 页码 -出版社
SPRINGER
DOI: 10.1007/JHEP10(2018)121
关键词
Jets
资金
- DOE [DE-SC0010008]
We apply computer vision with deep learning in the form of a convolutional neural network (CNN) to build a highly effective boosted top tagger. Previous work (the DeepTop tagger of Kasieczka et al) has shown that a CNN-based top tagger can achieve comparable performance to state-of-the-art conventional top taggers based on high-level inputs. Here, we introduce a number of improvements to the DeepTop tagger, including architecture, training, image preprocessing, sample size and color pixels. Our final CNN top tagger outperforms BDTs based on high-level inputs by a factor of approximate to 2-3 or more in background rejection, over a wide range of tagging efficiencies and fiducial jet selections. As reference points, we achieve a QCD background rejection factor of 500 (60) at 50% top tagging efficiency for fully-merged (non-merged) top jets with p(T) in the 800-900 GeV (350-450 GeV) range. Our CNN can also be straightforwardly extended to the classification of other types of jets, and the lessons learned here may be useful to others designing their own deep NNs for LHC applications.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据