论文标题
通过$ b \ bar {b} b \ bar {b} $最终状态对Higgs-pair生产的两higgs-doublet模型的敏感性
Sensitivity on Two-Higgs-Doublet Models from Higgs-Pair Production via $b\bar{b}b\bar{b}$ Final State
论文作者
论文摘要
众所周知，希格斯玻色子对生产是探测电动对称性破坏扇形的结构。我们说明了在两-higgs-doublet模型的框架中使用gluon融合过程$ pp \ to h \ to（b \ bar b）（b \ bar b）$ $，以及机器学习方法（三个流卷积神经网络）如何实质上可以改善信号背景歧视并从中改善相关的空间范围。我们表明，这种$ gg \ to hh \ to b \ bar b b \ b $过程可以进一步探测HIGGSSIGNALS和HIGGSBOUNDS在HL-LHC处的当前允许的参数空间。显示了IV型I类型的结果。
Higgs boson pair production is well known to probe the structure of the electroweak symmetry breaking sector. We illustrate using the gluon-fusion process $pp \to H \to h h \to (b\bar b) (b\bar b)$ in the framework of two-Higgs-doublet models and how the machine learning approach (three-stream convolutional neural network) can substantially improve the signal-background discrimination and thus improves the sensitivity coverage of the relevant parameter space. We show that such $gg \to hh \to b \bar b b\bar b$ process can further probe the currently allowed parameter space by HiggsSignals and HiggsBounds at the HL-LHC. The results for Types I to IV are shown.
