Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
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DOI：
10.1088/1748-0221/15/06/p06005
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发表时间：
2020-04
期刊：
影响因子：
--
通讯作者：
Cms Collaboration
中科院分区：
文献类型：
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作者：
Cms Collaboration
机器学习（ML）技术进行了探索，以识别和分类的强子衰变的高洛伦兹提升的W/Z/希格斯玻色子和顶夸克。没有ML的技术也进行了评估，并包括比较。各种算法的识别性能的特点，在模拟事件，并直接与数据进行比较。使用质子-质子碰撞数据在$\sqrt{s} =$13 TeV，对应于35.9 fb$^{-1}$的综合光度的算法进行验证。系统的不确定性进行评估，通过比较使用模拟和碰撞数据获得的结果。本文研究的新技术提供了显着的性能改进，非ML技术，降低了高达一个数量级的信号效率相同的背景速率。
Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at $\sqrt{s} =$ 13 TeV, corresponding to an integrated luminosity of 35.9 fb$^{-1}$. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.