论文标题
深度学习喷气图像作为LHC的光higgsino暗物质的探针
Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC
论文作者
论文摘要
超对称标准模型中的higgsino可以发挥暗物质粒子的作用。与自然标准结合使用，Higgsino质量参数预计将围绕Electroweak量表。在这项工作中，我们探索了通过LHC在机器学习中探测几乎退化的光希格斯诺诺斯的潜力。通过分析喷气图像和其他JET子结构信息，我们使用卷积神经网络（CNN）来增强信号意义。我们发现，我们的深度学习喷气图像方法可以根据传统的切割流量在高亮度LHC处提高先前的结果。
Higgsino in supersymmetric standard models can play the role of dark matter particle. In conjunction with the naturalness criterion, the higgsino mass parameter is expected to be around the electroweak scale. In this work, we explore the potential of probing the nearly degenerate light higgsinos with machine learning at the LHC. By analyzing jet images and other jet substructure information, we use the Convolutional Neural Network(CNN) to enhance the signal significance. We find that our deep learning jet image method can improve the previous result based on the conventional cut-flow by about a factor of two at the High-Luminosity LHC.
