Jet shapes with the broadening axis

By A. Larkoski, D. Neill, Jesse Thaler. 2014

230 citations recorded by the source corpus.

A bstractBroadening is a classic jet observable that probes the transverse momentum structure of jets. Traditionally, broadening has been measured with respect to the thrust axis, which is aligned along the (hemisphere) jet momentum to minimize the vector sum of transverse momentum within a jet. In this paper, we advocate measuring broadening with respect to the “broadening axis”, which is the direction that minimizes the scalar sum of transverse momentum within a jet. This approach eliminates many of the calculational complexities arising from recoil of the leading parton, and observables like the jet angularities become recoil-free when measured using the broadening axis. We derive a simple factorization theorem for broadening-axis observables which smoothly interpolates between the thrust-like and broadening-like regimes. We argue that the same factorization theorem holds for two-point energy correlation functions as well as for jet shapes based on a “winner-take-all axis”. Using kinked broadening axes, we calculate event-wide angularities in e+e− collisions with next-to-leading logarithmic resummation. Defining jet regions using the broadening axis, we also calculate the global logarithms for angularities within a single jet. We find good agreement comparing our calculations both to showering Monte Carlo programs and to automated resummation tools. We give a brief historical perspective on the broadening axis and suggest ways that broadening-axis observables could be used in future jet substructure studies at the Large Hadron Collider.

Subfields: Nuclear and High Energy Physics, Evaluation & Benchmarks

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Citations in the corpus

Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis (2017, methodology): “muthal angle, respectively. The sums run over the entire image. The quantities a and ˚ a are axis values determined with the one-pass k t axis selection using the winner-take-all combination scheme [42]. The distributions of m(I);p T(I), and ˝ 21(I) are shown in Fig.6for both GAN and Pythia images.”
Les Houches 2015: Physics at TeV Colliders Standard Model Working Group Report (2016, methodology): “3 [458] using the ee-variant of the anti-kt algorithm [326], with |~ p|-ordered winner-take-all recombination [431, 459, 460] to determine the jet axis n̂.”
Analytic boosted boson discrimination (2015, methodology): “We cut on the transverse momentum of the hardest jet, requiring pT ∈ [450, 550] GeV, and on the jet mass requiring mJ ∈ [80, 100] GeV.”
Jet-images — deep learning edition (2015, methodology): “In this application, the winner-takes-all axis [31] is used to deﬁne the axis in the τ 21 calculation.”
Systematics of quark/gluon tagging (2017, methodology): “Unlike standard E-scheme recombination [73], the winner-take-all scheme yields a jet axis n̂ that does not necessarily align with the jet three-momentum ~ p; this turns out to be a desirable feature for avoiding soft recoil effects [13, 29, 30, 74, 75].”
Les Houches 2017: Physics at TeV Colliders Standard Model Working Group Report (2017, methodology): “For β = 1, we recluster using the kt algorithm [817] with the winner-take-all recombination scheme [818].”
Introduction to Soft-Collinear Effective Theory (2014, background): “These recoil effects complicate the computation of broadening and recently an alternative definition of the usual event shapes has been proposed which uses the broadening axis, the axis which minimizes the scalar sum of the transverse momenta, to define the shapes, and is insensitive to recoil effec”
Power counting to better jet observables (2014, methodology): “6) The β = 1 minimized axes are also referred to as “broadening axes” [55, 59] as they correspond to axes that minimize the value of broadening [56, 64, 65].”
Looking Inside Jets (2019, background): “tion schemes, like the massless ptor Etschemes, have been used in the past but are not discussed here. Several jet-substructure applications make use of the winner-take-all (WTA) recombination scheme [52] where the result of the recombination of two particles has the rapidity, azimuth and mass of th”
Soft drop (2014, background): “The value α = 2 is related to jet thrust/mass [7, 91, 107], α = 1 is related to jet broadening/girth/width [25, 47], and arbitrary α > 0 is related to the recoil-free angularities [102].”
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, background): “[207], and is similar to the spherocity axis [208] for e+e− events.”
Energy flow networks: deep sets for particle jets (2018, background): “Deviations from the scale-invariant expectation of 2 are largest near the core of the jet, where non-perturbative physics or axis-recoil effects [119] become important.”
Jet substructure at the Large Hadron Collider (2018, methodology): “10 A different axis definition for the subjet axes is used in ATLAS when calculating τN , known as the-winner-takes-all axis [159], which is consistently found to perform slightly better than the standard subjet axis definition in tagging bosons.”
Factorization for groomed jet substructure beyond the next-to-leading logarithm (2016, methodology): “The energy correlation functions have the nice property that they are insensitive to recoil effects [20, 40] and do not include explicit axes in their definition.”
Gaining (mutual) information about quark/gluon discrimination (2014, methodology): “Our analytic calculations build on previous work calculating the substructure of quark and gluon jets [9, 29, 30, 34–36] and well as calculating angularities in e+e− event shapes [22, 28, 37].”