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arXiv 2203.14569hep-ph
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深度学习喷流图像作为LHC上的轻希格斯纳暗物质探测器

Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC

Huifang Lv, Daohan Wang, Lei Wu

摘要:

超对称标准模型中的Higgsino可以扮演暗物质粒子的角色。结合自然性准则,希格西诺质量参数预计将在电弱标度附近。在这项工作中,我们探索了在大型强子对撞机上用机器学习探测近简并光希格斯玻色子的潜力。通过分析射流图像和其他射流子结构信息,我们使用卷积神经网络(CNN)来增强信号的重要性。我们发现,我们的深度学习射流图像方法可以在高亮度LHC上将之前基于传统切割流的结果提高约两倍。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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