标题： 从奇异夸克标记到碎片化标记的机器学习方法 显示英文标题

标题： From strange-quark tagging to fragmentation tagging with machine learning

摘要： 我们应用先进的机器学习技术来解决大型强子对撞机（LHC）上的两个具有挑战性的喷注分类问题。 第一个是奇异夸克标记，特别是区分奇异夸克喷注和下夸克喷注。 第二个问题，我们称之为碎片化标记，涉及识别夸克的碎片化通道。 我们通过训练神经网络来区分包含底重子的底喷注和包含底介子的底喷注来说明这一点。 这两个问题的共同挑战在于，无论是夸克寿命和质量还是部分子簇射都无法提供区分工具，因此必须依赖于每种喷注中包含的强子类型分布及其运动学的差异。 对于这些分类任务，我们使用图注意力网络的不同变体和粒子变换器，它们将喷注和所有组成粒子的属性作为输入。 我们将它们的性能与一个使用简单变量的简单多层感知机进行比较。 我们发现更复杂的架构在$s$夸克与$d$夸克喷注区分方面没有显著提升，但在$b$重子与$b$介子喷注区分方面却带来了显著的提升。 显示英文摘要

摘要： We apply advanced machine learning techniques to two challenging jet classification problems at the LHC. The first is strange-quark tagging, in particular distinguishing strange-quark jets from down-quark jets. The second, which we term fragmentation tagging, involves identifying the fragmentation channel of a quark. We exemplify the latter by training neural networks to differentiate between bottom jets containing a bottom baryon and those containing a bottom meson. The common challenge in these two problems is that neither quark lifetimes and masses nor parton showering provide discriminating tools, making it necessary to rely on differences in the distributions of the hadron types contained in each type of jet and their kinematics. For these classification tasks, we employ variations of Graph Attention Networks and the Particle Transformer, which receive jet and all constituent properties as inputs. We compare their performance to a simple Multilayer Perceptron that uses simple variables. We find that the more sophisticated architectures do not improve $s$-quark versus $d$-quark jet differentiation by a significant amount, but they do lead to a significant gain in $b$-baryon versus $b$-meson jet differentiation.