Quark and gluon jet substructure

By Jason Gallicchio, M. Schwartz. Journal of High Energy Physics, 2012

149 citations recorded by the source corpus.

Distinguishing quark-initiated jets from gluon-initiated jets has the potential to significantly improve the reach of many beyond-the-standard model searches at the Large Hadron Collider and to provide additional tests of QCD. To explore whether quark and gluon jets could possibly be distinguished on an event-by-event basis, we perform a comprehensive simulation-based study. We explore a variety of motivated and unmotivated variables with a semi-automated multivariate approach. General conclusions are that at 50% quark jet acceptance efficiency, around 80%-90% of gluon jets can be rejected. Some benefit is gained by combining variables. Different event generators are compared, as are the effects of using only charged tracks to avoid pileup. Additional information, including interactive distributions of most variables and their cut efficiencies, can be found at http://jets.physics.harvard.edu/qvg.

Subfields: Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/9d7be34906a45a467f2898dc92196d76a29e48f2

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Citations in the corpus

Looking Inside Jets (2019, methodology): “s are gluon-dominated. This motivates the use of substructure tools to try and discriminate between the two. Some years ago, a wide range of discriminants has been systematically studied and compared [135]. It is not our goal to go through all the details of this study. Instead, we have selected a f”
Les Houches 2015: Physics at TeV Colliders Standard Model Working Group Report (2016, methodology): “We will also neglect quark/gluon discriminants that take into account azimuthal asymmetries within the jet, though observables like the covariance tensor [409] and 2-subjettiness [465,466] can improve quark/gluon discrimination.”
Energy correlation functions for jet substructure (2013, background): “[39, 73, 75, 77, 78] has not entered into these issues.”
Classification without labels: learning from mixed samples in high energy physics (2017, background): “In figure 5, we show ROC and significance improvement (SI) curves for 150k training events, where SI is a curve of q/ √ g at different q values [50].”
Energy flow networks: deep sets for particle jets (2018, methodology): “The fact that the PFNs outperform the EFN indicates that IRC-unsafe information is helpful for discrimination, which is not surprising in light of the fact that the constituent multiplicity is IRC unsafe and is known to be a good quark/gluon discriminant [85].”
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, background): “Particle multiplicity is among the most powerful observables for discrimination of quark and gluon jets [274, 275, 212], and further theoretical studies could provide understanding as to why it works so well and provide insight into the construction of new observables.”
Gaining (mutual) information about quark/gluon discrimination (2014, background): “[4, 5], one of the best single discriminant variables is λ0 (i.”
Deep learning in color: towards automated quark/gluon jet discrimination (2016, background): “Some general lessons from [24] and [25] were that there are essentially two complementary types of observables: shape and count observables.”
Systematics of quark/gluon tagging (2017, background): “[9] for an extensive catalog), but here we limit ourselves to a two-parameter family of generalized angularities”
ParticleNet: Jet Tagging via Particle Clouds (2019, methodology): “Methods based on the QCD theory have been proposed and continuously improved for discriminating quark and gluon jets [1–7], tagging jets originating from high-momentum heavy particles [8–18], etc.”
A hybrid strong/weak coupling approach to jet quenching (2014, background): “Unfortunately, although there has been substantial recent progress toward separating quark-initiated jets and gluon-initiated jets in proton-proton collisions [94–99], doing so in heavy ion collisions is sufficiently challenging that it does not”
Energy flow polynomials: a complete linear basis for jet substructure (2017, methodology): “Next, we consider observables based on the two-dimensional geometric moment tensor of the energy distribution in the (y, φ)-plane [49, 60]:”
Power counting to better jet observables (2014, background): “gluon discrimination is requires a detailed analysis of the effects of these order-1 parameters, which has been studied in several papers [43, 53, 89, 90].”