A continuous calibration of the ATLAS flavour-tagging classifiers via optimal transportation maps

TL;DR

This paper introduces a novel calibration method for ATLAS flavour-tagging algorithms using optimal transportation maps, enabling continuous, high-dimensional corrections to improve simulation accuracy in LHC analyses.

Contribution

It presents a new calibration procedure based on optimal transportation maps for jet-flavour classification probabilities in ATLAS data.

Findings

Closure achieved between simulation and observation after calibration

Calibration enables continuous, high-dimensional corrections for jet-flavour probabilities

Method improves the use of jet flavour information in LHC analyses

Abstract

A calibration of the ATLAS flavour-tagging algorithms using a new calibration procedure based on optimal transportation maps is presented. Simultaneous, continuous corrections to the $b$-jet, $c$-jet, and light-flavour jet classification probabilities from jet-tagging algorithms in simulation are derived for $b$-jets using $t\bar t \to e\mu\nu\nu bb$ data. After application of the derived calibration maps, closure between simulation and observation is achieved for jet flavour observables used in ATLAS analyses of Large Hadron Collider (LHC) Run 2 proton-proton collision data. This continuous calibration opens up new possibilities for the future use of jet flavour information in LHC analyses and also serves as a guide for deriving high-dimensional corrections to simulation via transportation maps, an important development for a broad range of inference tasks.