2026/07/27 by Gregorio de la Fuente, Jesse Thaler · 1 citation
Computer Science · Physics and Astronomy · #cs.LG #hep-ex #hep-ph
47 pages, 10 figures; our code is available at https://github.com/gregofuente/simplex_demixing
arxiv created 2026/07/27 · arxiv updated 2026/07/30
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.