Data-Driven, Geometry-Aware Optimal-Transport Calibration of Flavor Tagger
Yeonjoon Kim, Un-ki Yang

TL;DR
This paper introduces a geometry-aware, optimal transport-based framework for continuous, event-level flavor-tagger calibration that leverages control-region data and normalizing flows for improved accuracy.
Contribution
It formulates flavor-tagger calibration as an optimal transport problem on the probability simplex, incorporating a novel EM technique and geometry-aware transport maps.
Findings
Improved calibration closure in control regions.
Effective extraction of flavor-conditional target distributions.
Enhanced calibration accuracy over traditional methods.
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…
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