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Data-Driven, Geometry-Aware Optimal-Transport Calibration of Flavor Tagger
Yeonjoon Kim · 2026-05-05 · via cs.LG updates on arXiv.org

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Abstract: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 isometric log-ratio coordinate system. Because the quadratic Euclidean cost of Brenier transport in this coordinate system is equivalent to the Aitchison distance on the simplex, the learned map induces a minimal deformation under the Aitchison geometry. Furthermore, we extract flavor-conditional target distributions directly from control-region data using an expectation-maximization (EM) technique that simultaneously fits multiple control regions, models each flavor component with a normalizing flow, and estimates the regional mixture fractions. The extracted targets are subsequently used to learn flavor-factorized transport maps. Because the joint estimation of mixture fractions and flexible component densities admits weakly constrained directions, we further introduce a linearized feedback-operator analysis that propagates the fitted composition covariance into the extracted component densities, separating data-constrained modes from those dominated by the composition prior. The simulation-based closure study demonstrates improved closure in dedicated control regions and in independent validation mixtures.
Comments: 32 Pages, 12 Figures
Subjects: High Energy Physics - Experiment (hep-ex); Machine Learning (cs.LG); High Energy Physics - Phenomenology (hep-ph); Methodology (stat.ME)
Cite as: arXiv:2605.01363 [hep-ex]
  (or arXiv:2605.01363v1 [hep-ex] for this version)
  https://doi.org/10.48550/arXiv.2605.01363

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yeonjoon Kim [view email]
[v1] Sat, 2 May 2026 10:16:13 UTC (1,544 KB)

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