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arXiv:2301.06319hep-ex

Calibration of the light-flavour jet mistagging efficiency of the $b$-tagging algorithms with $Z$+jets events using 139 $\mathrm{fb}^{-1}$ of ATLAS proton-proton collision data at $\sqrt{s} = 13$ TeV

ATLAS Collaboration

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Abstract

The identification of $b$-jets, referred to as $b$-tagging, is an important part of many physics analyses in the ATLAS experiment at the Large Hadron Collider and an accurate calibration of its performance is essential for high-quality physics results. This publication describes the calibration of the light-flavour jet mistagging efficiency in a data sample of proton-proton collision events at $\sqrt{s}=13$ TeV corresponding to an integrated luminosity of 139 fb$^{-1}$. The calibration is performed in a sample of $Z$ bosons produced in association with jets. Due to the low mistagging efficiency for light-flavour jets, a method which uses modified versions of the $b$-tagging algorithms referred to as flip taggers is used in this work. A fit to the jet-flavour-sensitive secondary-vertex mass is performed to extract the scale factor from data, to correct the light-flavour jet mistagging efficiency in Monte Carlo simulations, while simultaneously correcting the $b$-jet efficiency. With this procedure, uncertainties coming from the modeling of jets from heavy-flavour hadrons are considerably lower than in previous calibrations of the mistagging scale factors, where they were dominant. The scale factors obtained in this calibration are consistent with unity within uncertainties.

arXiv comments
42 pages in total, author list starting page 25, 9 figures, 6 tables, published in EPJC. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/FTAG-2019-02/
Journal reference
Eur. Phys. J. C 83 (2023) 728
DOI
doi:10.1140/epjc/s10052-023-11736-z
Subjects
hep-exHigh Energy Physics, Experiment

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    • For better or worse the people working on this don't really use perplexity or accuracy to evaluate models. The target is whatever you'd get for those metrics if you used the discriminants that were provided in the dataset (i.e. the GN2v01 values). As for why accuracy and…

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