论文标题
质子和重离子碰撞中夸克和gluon喷气机的子结构的数据驱动提取
Data-driven extraction of the substructure of quark and gluon jets in proton-proton and heavy-ion collisions
论文作者
论文摘要
在重型离子碰撞中产生的夸克 - 格鲁隆等离子体中夸克和Gluon发射的喷气机的修改是一个长期存在的问题，尚未从实验中获得确切的答案。特别是，理论模型之间的夸克 - 格鲁隆等离子体的修饰大小不同。因此，完全数据驱动的技术对于公正的夸克和Gluon射流光谱和亚结构的提取至关重要。佐证了过去的结果，我证明了一种完全数据驱动的技术的能力，称为主题建模在分离夸克和Gluon对喷气式可观察物的贡献中的能力。数据驱动的主题分离结果可进一步用于提取喷气子结构，例如喷气形状和喷气片段化函数及其各自的QGP修改。此外，我建议使用机器学习构建的可观察物，并证明了提高可观察到的输入的可分离性的潜力。这项概念验证研究基于pyquen发电机的质子 - 普罗顿和重离子碰撞事件，在大型强子对撞机的运行4中可以访问统计数据。这些结果表明，有潜力测定夸克和Gluon-jet光谱，它们的子结构以及它们在QGP中的修饰。
The modification of quark- and gluon-initiated jets in the quark-gluon plasma produced in heavy-ion collisions is a long-standing question that has not yet received a definitive answer from experiments. In particular, the size of the modifications in the quark-gluon plasma differs between theoretical models. Therefore a fully data-driven technique is crucial for an unbiased extraction of the quark and gluon jet spectra and substructure. Corroborating past results, I demonstrate the capability of a fully data-driven technique called topic modeling in separating quark and gluon contributions to jet observables. The data-driven topic separation results can further be used to extract jet substructures, such as jet shapes and jet fragmentation function, and their respective QGP modifications. In addition, I propose the use of machine learning constructed observables and demonstrate the potential to increase separability for the input observable. This proof-of-concept study is based on proton-proton and heavy-ion collision events from the PYQUEN generator with statistics accessible in Run 4 of the Large Hadron Collider. These results suggest the potential for an experimental determination of quark- and gluon-jet spectra, their substructures, and their modification in the QGP.
