Thomas Flacke, Jeong Han Kim, Manuel Kunkel, Pyungwon Ko, Jun Seung Pi, Werner Porod, Leonard Schwarze
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引用次数: 0
Abstract
A bstract We propose a deep learning-based search strategy for pair production of doubly charged scalars undergoing three-body decays to $$ {W}^{+}t\overline{b} $$ W+tb¯ in the same-sign lepton plus multi-jet final state. This process is motivated by composite Higgs models with an underlying fermionic UV theory. We demonstrate that for such busy final states, jet image classification with convolutional neural networks outperforms standard fully connected networks acting on reconstructed kinematic variables. We derive the expected discovery reach and exclusion limit at the high-luminosity LHC.
我们提出了一种基于深度学习的搜索策略,用于在同符号轻子加多射流最终状态下经历三体衰变到$$ {W}^{+}t\overline{b} $$ W + t b¯的双带电标量的对生成。这个过程是由复合希格斯模型和一个潜在的费米子紫外理论驱动的。我们证明,对于如此繁忙的最终状态,卷积神经网络的射流图像分类优于作用于重构运动变量的标准全连接网络。推导了高亮度LHC的预期发现范围和排除极限。
期刊介绍:
The aim of the Journal of High Energy Physics (JHEP) is to ensure fast and efficient online publication tools to the scientific community, while keeping that community in charge of every aspect of the peer-review and publication process in order to ensure the highest quality standards in the journal.
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Mostly Strong Interactions (phenomenology).