Investigating the topology dependence of quark and gluon jets

Bright-Thonney, Samuel; Nachman, B. P.

doi:10.1007/jhep03(2019)098

Cited by 27 publications

(18 citation statements)

References 95 publications

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“…It is seen that this observables shows deviations up to 40 % in the large-R limit. Jet identification techniques to reveal whether the seed parton is a gluon or a quark [25,26] might be able to increase the discriminatory power even further.…”

Section: Soft Measures: Average Hadron Mass and Chargementioning

confidence: 99%

Soft modifications to jet fragmentation in high energy proton–proton collisions

Bierlich

2019

Physics Letters B

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The discovery of collectivity in proton-proton collisions, is one of the most puzzling outcomes from the first two runs at LHC, as it points to the possibility of creation of a Quark-Gluon Plasma, earlier believed to only be created in heavy ion collisions. One key observation from heavy ion collisions is still not observed in proton-proton, namely jet-quenching. In this letter it is shown how a model capable of describing soft collective features of proton-proton collisions, also predicts modifications to jet fragmentation properties. With this starting point, several new observables suited for the present and future hunt for jet quenching in small collision systems are proposed.

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Section: Soft Measures: Average Hadron Mass and Chargementioning

confidence: 99%

Soft modifications to jet fragmentation in high energy proton–proton collisions

Bierlich

2019

Physics Letters B

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“…1 Instead, a distance measure motivated by the fragmentation of quarks and gluons is used to collect constituents that were likely produced from the same initiating high-energy quark or gluon. This last sentence does not have a precise meaning because quark and gluon jets are not well-defined objects [47,48]. Due to the strength of the strong force, the energy flows from outgoing quarks and gluons are interconnected with each other and with the beam remnants.…”

Section: Introductionmentioning

confidence: 99%

Supervised jet clustering with graph neural networks for Lorentz boosted bosons

Nachman

2020

Phys. Rev. D

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Jet clustering is traditionally an unsupervised learning task because there is no unique way to associate hadronic final states with the quark and gluon degrees of freedom that generated them. However, for uncolored particles like W, Z, and Higgs bosons, it is possible to approximately (though not exactly) associate final state hadrons to their ancestor. By labeling simulated final state hadrons as descending from an uncolored particle, it is possible to train a supervised learning method to create boson jets. Such a method would operate on individual particles and identify connections between particles originating from the same uncolored particle. Graph neural networks are well-suited for this purpose as they can act on unordered sets and naturally create strong connections between particles with the same label. These networks are used to train a supervised jet clustering algorithm. The kinematic properties of these graph jets better match the properties of simulated Lorentz-boosted W bosons. Furthermore, the graph jets contain more information for discriminating W jets from generic quark jets. This work marks the beginning of a new exploration in jet physics to use machine learning to optimize the construction of jets and not only the observables computed from jet constituents.

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“…However, for isolated jets, the topology dependence is predicted to be much smaller than the difference between quark and gluon jets [102]. and h c i from the unfolding method, uncertainties in the extracted h g i and h q i distributions arise from the PDF choice, from the matrix elements, from the fragmentation model (due to flavor changing), and from the method nonclosure.…”

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confidence: 99%

“…The determination of the uncertainty from the choice of PDF uses the NNPDF uncertainty set [NNPDF 2.3 at LO in QCD and QED with α S ðm Z Þ ¼ 0.119] and the matrix-element uncertainty is estimated by comparing the nominal fractions from PYTHIA with those from HERWIG. 6 The nonclosure uncertainty is due to the small (subpercent level) differences between forward and central quark jets, as well as forward and central gluon jets, resulting from an η dependence in the jet fragmentation at a fixed jet p T [102]. When presenting the average properties in bins of jet p T , the binning correction described in Sec.…”

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confidence: 99%

Properties of jet fragmentation using charged particles measured with the ATLAS detector in pp collisions at s=13 TeV

Aad¹,

Abbott²,

Abbott³

et al. 2019

Phys. Rev. D

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This paper presents a measurement of quantities related to the formation of jets from high-energy quarks and gluons (fragmentation). Jets with transverse momentum 100 GeV < p T < 2.5 TeV and pseudorapidity jηj < 2.1 from an integrated luminosity of 33 fb −1 of ffiffi ffi s p ¼ 13 TeV proton-proton collisions are reconstructed with the ATLAS detector at the Large Hadron Collider. Charged-particle tracks with p T > 500 MeV and jηj < 2.5 are used to probe the detailed structure of the jet. The fragmentation properties of the more forward and the more central of the two leading jets from each event are studied. The data are unfolded to correct for detector resolution and acceptance effects. Comparisons with parton shower Monte Carlo generators indicate that existing models provide a reasonable description of the data across a wide range of phase space, but there are also significant differences. Furthermore, the data are interpreted in the context of quark-and gluon-initiated jets by exploiting the rapidity dependence of the jet flavor fraction. A first measurement of the charged-particle multiplicity using model-independent jet labels (topic modeling) provides a promising alternative to traditional quark and gluon extractions using input from simulation. The simulations provide a reasonable description of the quark-like data across the jet p T range presented in-this measurement, but the gluon-like data have systematically fewer charged particles than the simulation.

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Investigating the topology dependence of quark and gluon jets

Cited by 27 publications

References 95 publications

Soft modifications to jet fragmentation in high energy proton–proton collisions

Soft modifications to jet fragmentation in high energy proton–proton collisions

Supervised jet clustering with graph neural networks for Lorentz boosted bosons

Properties of jet fragmentation using charged particles measured with the ATLAS detector in pp collisions at s=13 TeV

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