发表机构
Massachusetts Institute of Technology; Center for Theoretical Physics -- a Leinweber Institute; The NSF AI Institute for Artificial Intelligence and Fundamental Interactions（麻省理工学院; 理论物理中心——莱因韦伯研究所; 美国国家科学基金会人工智能与基本相互作用人工智能研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
对撞机物理中为多种喷注味提供定义有挑战，此前无超两类喷注的推广。本文引入“单纯形解混”框架，先在玩具问题演示，后提出标记和探测策略用于提取轻味类别，为大型强子对撞机数据驱动提取喷注味属性打开大门。
AI 中文摘要
在对撞机物理中，为多种喷注味提供一个实用的强子级定义一直是一个长期存在的挑战。先前的工作引入了夸克和胶子喷注的数据驱动的操作定义，但目前还不存在超越两种喷注类别的稳健推广。为了解决这个问题，我们引入了一个名为“单纯形解混”的机器学习框架，以在最小约束下从M个数据样本（或混合物）中提取T种喷注味（或统计文献中的主题）。直观地说，我们的过程识别数据中最大可分的类别，将M个混合物上的多类别分类器转化为一个有T个顶点的有界几何对象。我们首先在一个玩具问题上演示我们的过程，从三个纯样本的合成混合物中推断下夸克、上夸克和胶子喷注的真实水平分数。然后，我们提出一种标记和探测策略，在一个更现实的涉及双喷注产生的对撞机设置中提取多种轻味类别。正如预期的那样，喷注味的可识别性取决于它们在样本中的相对丰度以及分类器架构可获得的强子级信息。我们的工作为在大型强子对撞机上数据驱动地提取多种喷注味属性打开了大门。
英文摘要
Providing a practical and hadron-level definition of multiple jet flavors has been a long-standing challenge in collider physics. Previous work has introduced a data-driven, operational definition of quark and gluon jets, but no robust generalization beyond two jet categories presently exists. To address this, we introduce a machine-learning framework called "simplex demixing'' to extract $T$ jet flavors (or topics in the statistics literature) from $M$ data samples (or mixtures) with minimal constraints. Intuitively, our procedure identifies the maximally separable categories in the data, translating a multi-category classifier on the $M$ mixtures into a bounded geometric object with $T$ vertices. We first demonstrate our procedure on a toy problem to infer the truth-level fractions of down-quark, up-quark, and gluon jets from synthetic mixtures of the three pure samples. We then propose a tag-and-probe strategy to extract multiple light-flavor categories in a more realistic collider setting involving dijet production. As expected, the identifiability of jet flavors depends on their relative abundance in the samples and the hadron-level information available to the classifier architecture. Our work opens the door to data-driven extractions of multiple jet flavor properties at the Large Hadron Collider.
Comments47 pages, 10 figures; our code is available at https://github.com/gregofuente/simplex_demixing
