Power counting to better jet observables

By A. Larkoski, I. Moult, D. Neill. 2014

265 citations recorded by the source corpus.

A bstractOptimized jet substructure observables for identifying boosted topologies will play an essential role in maximizing the physics reach of the Large Hadron Collider. Ideally, the design of discriminating variables would be informed by analytic calculations in perturbative QCD. Unfortunately, explicit calculations are often not feasible due to the complexity of the observables used for discrimination, and so many validation studies rely heavily, and solely, on Monte Carlo. In this paper we show how methods based on the parametric power counting of the dynamics of QCD, familiar from effective theory analyses, can be used to design, understand, and make robust predictions for the behavior of jet substructure variables. As a concrete example, we apply power counting for discriminating boosted Z bosons from massive QCD jets using observables formed from the n-point energy correlation functions. We show that power counting alone gives a definite prediction for the observable that optimally separates the background-rich from the signal-rich regions of phase space. Power counting can also be used to understand effects of phase space cuts and the effect of contamination from pile-up, which we discuss. As these arguments rely only on the parametric scaling of QCD, the predictions from power counting must be reproduced by any Monte Carlo, which we verify using Pythia 8 and Herwig++. We also use the example of quark versus gluon discrimination to demonstrate the limits of the power counting technique.

Subfields: Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/4dacf2e64e8411941bd1a1d84046b35ea03e9a91

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In Nuclear and High Energy Physics

Citations in the corpus

Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks (2016, result): “The uncertainties from the varying the parton shower for the jet images are thus of similar size to those associated other more common variables, such as those found in theoretical studies of the D2 tagger [63, 64] and those found by the ATLAS Collaboration in [65] in searches for boosted W-bosons.”
Les Houches 2017: Physics at TeV Colliders Standard Model Working Group Report (2017, methodology): “3 Towards Improved Performance and Robustness for ATLAS and CMS The strategies used by ATLAS and CMS, namely trimmed D2 [780, 787] and SoftDropped N2 [796] with DDT [800] are specific examples of the broader approaches to two-prong tagging discussed above, and therefore our study allows us to gain i”
The Lund jet plane (2018, methodology): “mised choice of jet-shape discriminator. For 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”
Physics at a 100 TeV pp collider: Higgs and EW symmetry breaking studies (2016, methodology): “For the boosted and intermediate categories, which involve large-R jets, a number of jet substructure variables [225, 226] are used in the analysis: the kT -splitting scale [151, 227], the ratio of 2-to-1 subjettiness τ21 [228, 229], and the ratios of energy correlation functions (ECFs) C (β) 2 [230”
Analytic boosted boson discrimination (2015, methodology): “[66] by measuring e (α) 2 and e (α) 3 , but here we consider the phase space defined by e (β) 2 and e (α) 3 with α and β in general different.”
Looking Inside Jets (2019, background): “ark tagging for the nal set of DNN and BDT input observables [207]. pt and mcomb are the transverse momentum of the jet and the combined jet mass [210]. e 3, C 2 and D 2 are energy correlation ratios [143,211]. ˝i and ˝ij are N-subjettiness variables and ratios respectively. RFW 2 is a Fox-Wolfram m”
Energy flow polynomials: a complete linear basis for jet substructure (2017, background): “Previous efforts to define classes of observables around organizing principles include: the jet energy moments and related Zernike polynomials to classify energy flow observables [49]; a pixelated jet image [50] to represent energy deposits in a calorimeter; the energy correlation functions (ECFs) [”
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, background): “while for energy correlation functions, the optimal ratio is called D (α) 2 [233]:”
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 ”