论文标题
使用机器学习技术鉴定重，能量，强调衰减的颗粒
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
论文作者
论文摘要
探索了机器学习（ML）技术，以识别和分类高度洛伦兹促进的w/z/higgs玻色子和顶级夸克。没有ML的技术也已进行了评估，并包括用于比较。在模拟事件中表征了多种算法的识别性能，并将其直接与数据进行比较。该算法使用$ \ sqrt {s} = $ 13 tev的质子 - 普罗顿碰撞数据验证，对应于35.9 fb $^{ - 1} $的集成光度。通过比较使用仿真和碰撞数据获得的结果来评估系统的不确定性。本文研究的新技术对非ML技术提供了显着的性能改进，从而在相同的信号效率下最多将背景速率降低了一个数量级。
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.
