将机器学习技术应用于中等长度级联衰变
Applying Machine Learning Techniques To Intermediate-Length Cascade Decays
AI总结：
针对标准模型扩展中伴随粒子级联衰变难以发现的问题，利用机器学习技术确定四终态粒子衰变拓扑的最佳运动学可观测量，并量化自旋确定准确度与质量测量精度。
摘要：
在带有伴随粒子的标准模型扩展的对撞机唯象学中，级联衰变普遍发生，当新粒子的谱被压缩且信号截面较低时，它们可能难以被发现。达到发现水平的显著性并测量在级联衰变中作为中间态出现的新粒子的性质是一个长期存在的问题，某些衰变拓扑的分析技术已经得到优化。我们将注意力集中在具有四个终态粒子的基准衰变拓扑上，该拓扑仍有改进空间，且多维分析技术已被证明在过去是有效的。利用机器学习技术，我们确定了用于发现、自旋确定和质量测量的最佳运动学可观测量。与过去的工作一致，我们确认运动学可观测量$Δ_4$非常有效。我们量化了自旋确定的可达到准确度和质量测量的精度作为信号大小的函数。
英文摘要：
In the collider phenomenology of extensions of the Standard Model with partner particles, cascade decays occur generically, and they can be challenging to discover when the spectrum of new particles is compressed and the signal cross section is low. Achieving discovery-level significance and measuring the properties of the new particles appearing as intermediate states in the cascade decays is a longstanding problem, with analysis techniques for some decay topologies already optimized. We focus our attention on a benchmark decay topology with four final state particles where there is room for improvement, and where multidimensional analysis techniques have been shown to be effective in the past. Using machine learning techniques, we identify the optimal kinematic observables for discovery, spin determination and mass measurement. In agreement with past work, we confirm that the kinematic observable $Δ_4$ is highly effective. We quantify the achievable accuracy for spin determination and for the precision for mass measurements as a function of the signal size.
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