Performance of b-jet identification in the ATLAS experiment

By Atlas Collaboration. 2015

518 citations recorded by the source corpus.

The identification of jets containing b hadrons is important for the physics programme of the ATLAS experiment at the Large Hadron Collider. Several algorithms to identify jets containing b hadrons are described, ranging from those based on the reconstruction of an inclusive secondary vertex or the presence of tracks with large impact parameters to combined tagging algorithms making use of multi-variate discriminants. An independent b-tagging algorithm based on the reconstruction of muons inside jets as well as the b-tagging algorithm used in the online trigger are also presented. The b-jet tagging efficiency, the c-jet tagging efficiency and the mistag rate for light flavour jets in data have been measured with a number of complementary methods. The calibration results are presented as scale factors defined as the ratio of the efficiency (or mistag rate) in data to that in simulation. In the case of b jets, where more than one calibration method exists, the results from the various analyses have been combined taking into account the statistical correlation as well as the correlation of the sources of systematic uncertainty.

Subfields: Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/23aa44327a506f9ab534030c9c4a5923b4b6a3c2

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In Nuclear and High Energy Physics

Citations in the corpus

Beyond the Standard Model Physics at the HL-LHC and HE-LHC (2018, background): “A constant 10% mis-tag rate was assumed, which is sufficiently realistic [725] for large pT (jet).”
JUNIPR: a framework for unsupervised machine learning in particle physics (2018, background): “The simplest architecture explored has been the fullyconnected network, which has successfully been applied in a wide variety of contexts, such as in identifying and splitting clusters from multiple particles in the pixel detector [10], in b-tagging [11], and in τ -identification [12].”
Measurements of Higgs boson properties in the diphoton decay channel with 36 fb$^{-1}$ of $pp$ collision data at $\sqrt{s} = 13$ TeV with the ATLAS detector (2018, unknown): “The corresponding uncertainties in the identification of jets originating from c-quarks, light quarks and gluons are taken directly from Run 1 studies [97], with additional uncertainties to cover the extrapolation to Run 2 conditions.”
Search for electroweak production of supersymmetric states in scenarios with compressed mass spectra at √s = 13 TeV with the ATLAS detector (2017, unknown): “[113] ATLAS Collaboration, Performance of b-jet identification in the ATLAS experiment, J.”
Search for the standard model Higgs boson produced in association with top quarks and decaying into a bb¯ pair in pp collisions at √s=13 TeV with the ATLAS detector (2018, methodology): “Jets are tagged as containing b-hadrons through a multivariate b-tagging algorithm (MV2c10) that combines information from an impact-parameter-based algorithm, from the explicit reconstruction of an inclusive secondary vertex and from a multi-vertex fitter that attempts to reconstruct the b- to c-ha”
Jet substructure at the Large Hadron Collider (2018, methodology): “(a) Subjet b-tagging [136, 156, 157, 158, 159, 160], where ‘standard’ b-tagging is applied to each of the subjets (the standard for CMS is the CSVv2 algorithm [154], and for ATLAS is MV2c20 [155]).”
Evidence for the associated production of the Higgs boson and a top quark pair with the ATLAS detector (2017, unknown): “[88] ATLAS Collaboration, Performance of b-jet identification in the ATLAS experiment, J.”
Search for electroweak production of supersymmetric particles in final states with two or three leptons at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\odd (2018, methodology): “Identification of jets containing b-hadrons (b-jets), so called b-tagging, is performed with the MV2c10 algorithm, a multivariate discriminant making use of track impact parameters and reconstructed secondary vertices [84, 85].”