论文标题
在LHC的3-3-1模型中对逆Seesaw的深度学习分析
Deep learnig analysis of the inverse seesaw in a 3-3-1 model at the LHC
论文作者
论文摘要
逆Seesaw是一种真正的TEV量表Seesaw机制。在IT中，具有EV量的质量的积极中微子需要在KEV量表上明确违反Lepton数字，并以TEV量表以重中微子的形式出现新物理学的存在。因此，这是一种现象学上可行的Seesaw机制，因为可以在LHC处探测其特征。此外，它已成功嵌入标准模型的量规扩展中，作为带有右手中微子的3-3-1模型。在这项工作中，我们将这种机制的实施重新审视到3-3-1模型中，并采用深度学习分析在LHC处探测了这种设置，并且作为主要结果，如果在下一个以14个TEV运行的LHC中未检测到它的签名，那么，vector boson $ z^{\ prime}的3-3-1 $比4 te tevs tevs a tevs a tevs teve boson $ z^{\ prime} $。
Inverse seesaw is a genuine TeV scale seesaw mechanism. In it active neutrinos with masses at eV scale requires lepton number be explicitly violated at keV scale and the existence of new physics, in the form of heavy neutrinos, at TeV scale. Therefore it is a phenomenologically viable seesaw mechanism since its signature may be probed at the LHC. Moreover it is successfully embedded into gauge extensions of the standard model as the 3-3-1 model with the right-handed neutrinos. In this work we revisit the implementation of this mechanism into the 3-3-1 model and employ deep learning analysis to probe such setting at the LHC and, as main result, we have that if its signature is not detected in the next LHC running with energy of 14 TeVs, then, the vector boson $Z^{\prime}$ of the 3-3-1 model must be heavier than 4 TeVs.
