论文标题
使用机器学习提高顶级夸克标签和极化测量
Boosted top quark tagging and polarization measurement using machine learning
论文作者
论文摘要
机器学习技术用于将喷气式用作图像，以探索增强的顶级夸克标签的性能。使用基于卷积神经网络（CNN）的技术以及增强的决策树（BDT），在顶级夸克衰减的Hadronic和Leptonic通道中都研究了标签性能。这种计算机视觉方法还用于区分左和右两极分化的顶部夸克。在这种情况下，提出了一个实验可测量的不对称变量来估计极化。结果表明，基于CNN的分类器比标准运动学变量对顶级夸克极化更敏感。据观察，松性通道中的整体标记性能比耐药案例更好，前者也可以更好地研究偏振化。
Machine learning techniques are used for treating jets as images to explore the performance of boosted top quark tagging. Tagging performances are studied in both hadronic and leptonic channels of top quark decay, employing a convolutional neural network (CNN) based technique along with boosted decision trees (BDT). This computer vision approach is also applied to distinguish between left and right polarized top quarks. In this context, an experimentally measurable asymmetry variable is proposed to estimate the polarization. Results indicate that the CNN based classifier is more sensitive to top quark polarization than the standard kinematic variables. It is observed that the overall tagging performance in the leptonic channel is better than the hadronic case, and the former also serves as a better probe for studying polarization.
