Energy correlation functions for jet substructure

By A. Larkoski, G. Salam, Jesse Thaler. 2013

406 citations recorded by the source corpus.

A bstractWe show how generalized energy correlation functions can be used as a powerful probe of jet substructure. These correlation functions are based on the energies and pair-wise angles of particles within a jet, with (N + 1)-point correlators sensitive to N-prong substructure. Unlike many previous jet substructure methods, these correlation functions do not require the explicit identification of subjet regions. In addition, the correlation functions are better probes of certain soft and collinear features that are masked by other methods. We present three Monte Carlo case studies to illustrate the utility of these observables: 2-point correlators for quark/gluon discrimination, 3-point correlators for boosted W /Z/Higgs boson identification, and 4-point correlators for boosted top quark identification. For quark/gluon discrimination, the 2-point correlator is particularly powerful, as can be understood via a next-to-leading logarithmic calculation. For boosted 2-prong resonances the benefit depends on the mass of the resonance.

Subfields: Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/81721edbee586474387abff22c741dad70847ce8

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In Nuclear and High Energy Physics

Citations in the corpus

The Lund jet plane (2018, methodology): “or this purpose, we apply the SoftDrop algorithm (= 2, z cut = 0:05) and use the resulting groomed jet to calculate both the jet mass and the D 2 observable [13] with = 2 (itself very similar to C 2 [11] for any given jet mass). The jet mass and D 2 are then given as an input to a boosted-decision t”
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, methodology): “[202] demonstrated that, from calculations to NLL accuracy, recoil-free observables like the energy correlation functions are more powerful quark versus gluon jet discriminants than their recoil-sensitive counterparts.”
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”
Deep learning in color: towards automated quark/gluon jet discrimination (2016, unknown): “(cid:15) Two-Point Moment [54]: , where the value (cid:12) = 0 : 2 is used.”
Jet Substructure Classification in High-Energy Physics with Deep Neural Networks (2016, methodology): “Then, the trimmed jet’s constituents are used to calculate the other substructure variables, N -subjettiness [9, 35] τ 21 , and the energy correlation functions [10, 36] C 2 , C β=2 2 , D β=1 2 , and D β=2 2 .”
Power counting to better jet observables (2014, methodology): “Second, because all angles in the energy correlation functions are measured between pairs of particles, e (β) n is insensitive to recoil or referred to as “recoil-free” [53, 56– 59].”
Analytic boosted boson discrimination (2015, methodology): “For studying the two-prong structure of a jet, we will need the 2- and 3-point energy correlation functions, which we define for e+e− collisions as [65]1”
Towards an understanding of jet substructure (2013, background): “It is the first time, to our knowledge, that such a feature is observed, and (21)In this context it may be beneficial to study a range of variables, such as N -subjettiness [26] and energy correlations [32], or even combinations of observables as done in refs.”
Looking Inside Jets (2019, methodology): “[143] and we refer the reader to this discussion for further details.”
Systematics of quark/gluon tagging (2017, methodology): “For the IRC-safe case of κ = 1, there is an alternative version of the angularities based on energy correlation functions [13] (see also [75, 88, 89]),”
Jet substructure at the Large Hadron Collider (2018, background): “In addition to the comprehensive studies of grooming options [57, 74, 86, 94, 95], ATLAS and CMS both investigated the discrimination powers for a plethora of jet substructure variables, including N -subjettiness [110, 111], Qjet volatility [154], ratios of energy correlation functions C 2 [117], D ”
Gaining (mutual) information about quark/gluon discrimination (2014, background): “[9], the qualitative β-dependence is the same at NLL compared to the two parton shower programs, but there are significant numerical differences.”
Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks (2016, background): “[5] on studying jet substructure in Higgs boson associated production, a multitude of taggers and variables related to substructure have been proposed [5–12].”
Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd–27th of July 2012 (2013, unknown): “[57].”