论文标题
通过机器学习识别重离子碰撞中的淬火喷气机
Identifying quenched jets in heavy ion collisions with machine learning
论文作者
论文摘要
超相关重离子碰撞中喷气子结构的测量表明，与夸克gluon等离子体的相互作用可以改变喷气淋浴过程。可以通过现代数据驱动技术探索喷气机的硬结构的修改。在这项研究中，设计了一种机器学习方法来识别淬火喷气机的识别。喷气淋浴过程是用喷气淬火模型珠宝模拟的，并非淬灭模型Pythia 8.顺序的子结构变量从乘坐有序的序列后从喷气聚类历史记录中提取，并用于训练在长期短期记忆网络顶部建立的神经网络。我们表明，这种方法成功地识别出在重离子碰撞中产生的软颗粒的大颗粒背景的情况下的淬灭效果。
Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by the interaction with quark gluon plasma. Modifications of the hard substructure of jets can be explored with modern data-driven techniques. In this study, a machine learning approach to the identification of quenched jets is designed. Jet showering processes are simulated with a jet quenching model Jewel and a non-quenching model Pythia 8. Sequential substructure variables are extracted from the jet clustering history following an angular-ordered sequence and are used in the training of a neural network built on top of a long short-term memory network. We show that this approach successfully identifies the quenching effect in the presence of the large uncorrelated background of soft particles created in heavy ion collisions.
