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Soft drop groomed jet angularities at the LHC

机译:大型强子对撞机的软滴修饰喷嘴角度

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Jet angularities are a class of jet substructure observables where a continuous parameter is introduced in order to interpolate between different classic observables such as the jet mass and jet broadening. We consider jet angularities measured on an inclusive jet sample at the LHC where the soft drop grooming procedure is applied in order to remove soft contaminations from the jets. The soft drop algorithm allows for a precise comparison between theory and data and could be used to extract the QCD strong coupling constantαsfrom jet substructure data in the future. We develop a framework to realize the resummation of all relevant large logarithms at the next-to-leading logarithmic (NLL) accuracy. To demonstrate that the developed formalism is suitable for the extraction ofαs, we extend our calculations to next-to-next-to-leading logarithm (NNLL) for the jet mass case. Overall, we find good agreement between our NLL numerical results and Pythia simulations for LHC kinematics and we observe an improved agreement when the NNLL components are included. In addition, we expect that groomed jet angularities will be a useful handle for studying the modification of jets in heavy-ion collisions.
机译:射流角是一类射流子结构可观测物,其中引入了连续参数以便在不同的经典可观测物(例如射流质量和射流展宽)之间进行插值。我们考虑在大型强子对撞机(LHC)上对包括在内的射流样品进行测量的射流角度,在此应用了软滴修饰程序,以去除射流中的软污染物。软液滴算法可以在理论和数据之间进行精确比较,并且将来可以用于从射流子结构数据中提取QCD强耦合常数αs。我们开发了一个框架,以实现次要的对数(NLL)精度恢复所有相关的大对数。为了证明发达的形式主义适合于α的提取,我们将计算结果扩展到了喷射质量情况下的下一个对数前导对数(NNLL)。总体而言,我们发现我们的NLL数值结果与LHC运动学的Pythia模拟之间有很好的一致性,并且当包含NNLL组件时,我们观察到了改进的一致性。此外,我们希望修饰后的射流角度将是研究重离子碰撞中射流变化的有用方法。

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