2021
DOI: 10.48550/arxiv.2105.04582
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Identifying the Quantum Properties of Hadronic Resonances using Machine Learning

Jakub Filipek,
Shih-Chieh Hsu,
John Kruper
et al.

Abstract: With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also be used to identify its quantum numbers. Convolutional neural networks (CNNs) using jet-images can significantly improve upon existing techniques to identify the quantum chromodynamic (QCD) ('color') as well as the spin of a two-prong resonance using its substructure. Addit… Show more

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Cited by 4 publications
(4 citation statements)
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“…Fortunately, in the last years there has been tremendous progress on quark-gluon tagging studies exploiting jet substructure properties with machine learning techniques [42]. The latest LHC results reach ε g ≈ 60% gluon efficiencies with ε mistag q→g ≈ 10% false positive rates using advanced multivariate analyses [43,44], or ε mistag q→g ≈ 7% [45] further exploiting Lund jet plane information [46]. Reaching mistagging rates down to ε mistag q→g ≈ 1%, while keeping large gluon reconstruction efficiencies, appears feasible in the clean and kinematically constrained QCD environment of future e + e − machines, in particular taking advantage of the very large samples of Z → qq(g) events at the Z pole, and the O(10 5 ) H → gg events collected during the e + e − → ZH runs, available for dedicated studies of the different colour, radiation, spin, charge, hadronization properties of quark and gluon jets [47][48][49].…”
Section: Multivariate Analysis (Mva) Per Channelmentioning
confidence: 99%
“…Fortunately, in the last years there has been tremendous progress on quark-gluon tagging studies exploiting jet substructure properties with machine learning techniques [42]. The latest LHC results reach ε g ≈ 60% gluon efficiencies with ε mistag q→g ≈ 10% false positive rates using advanced multivariate analyses [43,44], or ε mistag q→g ≈ 7% [45] further exploiting Lund jet plane information [46]. Reaching mistagging rates down to ε mistag q→g ≈ 1%, while keeping large gluon reconstruction efficiencies, appears feasible in the clean and kinematically constrained QCD environment of future e + e − machines, in particular taking advantage of the very large samples of Z → qq(g) events at the Z pole, and the O(10 5 ) H → gg events collected during the e + e − → ZH runs, available for dedicated studies of the different colour, radiation, spin, charge, hadronization properties of quark and gluon jets [47][48][49].…”
Section: Multivariate Analysis (Mva) Per Channelmentioning
confidence: 99%
“…[5] analyzed the radiation patterns inside jets to separate singlet from octet color flows. Similar color flow ideas have been applied to distinguishing color octet and singlet dijet events [4] and top pair tagging [6] and many other new physics searches [7,[93][94][95]. This is also the basic idea of the rapidity gap [96].…”
Section: Jhep08(2023)173mentioning
confidence: 90%
“…Fortunately, in the last years there has been tremendous progress on quark-gluon tagging studies exploiting jet substructure properties with machine learning techniques [41]. The latest LHC results reach ε g ≈ 60% gluon efficiencies with ε mistag q→g ≈ 10% false positive rates using advanced multivariate analyses [42,43], or ε mistag q→g ≈ 7% [44] further exploiting Lund jet plane information [45]. Reaching mistagging rates down to ε mistag q→g ≈ 1%, while keeping large gluon reconstruction efficiencies, appears feasible in the clean and kinematically constrained QCD environment of future e + e − machines, in particular taking advantage of the very large samples of Z → qq(g) events at the Z pole, and the O(10 5 ) H → gg events collected during the e + e − → ZH runs, available for dedicated studies of the different colour, radiation, spin, charge, hadronization properties of quark and gluon jets [46,47,48].…”
Section: Event Reconstruction and Preselectionmentioning
confidence: 99%