STAR在√s=200 GeV质子-质子碰撞中对CollinearDrop喷注质量及其与SoftDrop修饰喷注亚结构可观测量关联的测量
Measurement of CollinearDrop jet mass and its correlation with SoftDrop groomed jet substructure observables in $\sqrt{s}=200$ GeV $pp$ collisions by STAR
AI总结：
STAR在200 GeV质子-质子碰撞中用MultiFold修正探测器效应，测量CollinearDrop喷注质量及其与SoftDrop修饰亚结构的关联，揭示非微扰贡献增强及修饰程度与首次硬劈裂角尺度的反相关。
摘要：
喷注亚结构变量旨在揭示产生喷注的部分子碎裂和强子化过程的细节。通过移除共线辐射同时保留软辐射成分，可以构建CollinearDrop喷注可观测量，这类可观测量对喷注内部的软相空间具有增强的敏感性。我们展示了在STAR于√s=200 GeV的质子-质子碰撞中进行的CollinearDrop喷注测量，该测量使用机器学习方法MultiFold对探测器效应进行了修正，并研究了其与修饰后喷注可观测量的关联。我们证明，通过选择较高的CollinearDrop喷注质量分数，可以显著增强具有较大非微扰贡献的喷注数量。此外，我们观察到修饰程度与喷注第一次硬劈裂的角度尺度之间存在反相关。
英文摘要：
Jet substructure variables aim to reveal details of the parton fragmentation and hadronization processes that create a jet. By removing collinear radiation while maintaining the soft radiation components, one can construct CollinearDrop jet observables, which have enhanced sensitivity to the soft phase space within jets. We present a CollinearDrop jet measurement, corrected for detector effects with a machine learning method, MultiFold, and its correlation with groomed jet observables, in $pp$ collisions at $\sqrt{s}=200$ GeV at STAR. We demonstrate that the population of jets with a large non-perturbative contribution can be significantly enhanced by selecting on higher CollinearDrop jet mass fractions. In addition, we observe an anti-correlation between the amount of grooming and the angular scale of the first hard splitting of the jet.
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