论文标题
检测矢量玻色子散射中的异常
Detecting anomaly in vector boson scattering
论文作者
论文摘要
精确测量矢量玻色子散射（VBS）是了解标准模型（SM）的电子对称破坏并检测SM之外的新物理学的重要步骤。我们提出了一个神经网络，将VBS的特征压缩为三维潜在空间。 SM预测和实验数据的一致性通过潜在空间中的BINNED对数可能分析进行了测试。我们将表明，该网络能够区分DiLeptonic通道和半leptonic频道的$ WWJJ $生产的不同极化模式。该方法还用于限制有效的场理论和两个HIGGS Doublet模型。结果表明该方法对促成VBS的通用新物理学敏感。
Measuring the vector boson scattering (VBS) precisely is an important step towards understanding the electroweak symmetry breaking of the standard model (SM) and detecting new physics beyond the SM. We propose a neural network which compress the features of the VBS into three dimensional latent space. The consistency of the SM prediction and the experimental data is tested by the binned log-likelihood analysis in the latent space. We will show that the network is capable of distinguish different polarization modes of $WWjj$ production in both dileptonic channel and semi-leptonic channel. The method is also applied to constrain the effective field theory and two Higgs Doublet Model. The results demonstrate that the method is sensitive to generic new physics contributing to the VBS.
