Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders
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 $T$...
| Authors | Gregorio de la Fuente, Jesse Thaler |
|---|---|
| Primary category | hep-ph |
| Published | 27 July 2026 |
| Last updated at source | 9 October 2026 |
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