Data-Driven, Geometry-Aware Optimal-Transport Calibration of Flavor Tagger
Flavor-tagging calibrations are often provided either as scale factors measured at a finite set of working points or as binned corrections to a chosen one-dimensional discriminant. However, this approach falls short of providing continuous, event-level calibration across the full multicomponent outputs of modern taggers. This limitation leads to information loss in analyses that demand high-performance flavor tagging, restricting analyses to a limited set of predefined variables. In this work, we propose a geometry-aware framework that formulates flavor-tagger calibration as an optimal transport problem on the probability simplex. The transport maps are parameterized and trained in the is...
| Authors | Yeonjoon Kim, Un-ki Yang |
|---|---|
| Primary category | hep-ex |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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