Identifying boosted objects with N-subjettiness

By Jesse Thaler, K. Tilburg. 2010

944 citations recorded by the source corpus.

We introduce a new jet shape — N-subjettiness — designed to identify boosted hadronically-decaying objects like electroweak bosons and top quarks. Combined with a jet invariant mass cut, N-subjettiness is an effective discriminating variable for tagging boosted objects and rejecting the background of QCD jets with large invariant mass. In efficiency studies of boosted W bosons and top quarks, we find tagging efficiencies of 30% are achievable with fake rates of 1%. We also consider the discovery potential for new heavy resonances that decay to pairs of boosted objects, and find significant improvements are possible using N-subjettiness. In this way, N-subjettiness combines the advantages of jet shapes with the discriminating power seen in previous jet substructure algorithms.

Subfields: Nuclear and High Energy Physics, AI for Physical Sciences

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Citations in the corpus

Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis (2017, background): “Three such features of a jet image I are the mass m, transverse momentum pT, and n-subjettiness 21 [40]:”
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 .”
Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks (2016, methodology): “4 the ROC curves we obtain for the trimmed jet mass and the n -subjettiness ratio τ 21 ≡ τ 2 /τ 1 [10] which is often used as a discriminating variable in studies of jet substructure [62].”
Jet substructure at the Tevatron and LHC: new results, new tools, new benchmarksReport prepared by the participants of the BOOST 2011 Workshop at Princeton University, 22–26 May 2011. L Asquith (lasquith@hep.anl.gov), S Rappoccio (rappocc@fnal.gov) and C K Vermilion (verm@uw.edu), editors. (2011, methodology): “• N -subjettiness is a useful variable for jet substructure [38, 56].”
N-jettiness subtractions for NNLO QCD calculations (2015, methodology): “9), which has been used for example in the application to jet substructure [79, 80].”
Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV (2017, background): “A variable sensitive to the substructure is the N-subjettiness, τN [47], which is a jet shape variable, computed under the assumption that the jet has N subjets, and it is defined as the pTweighted distance between each jet constituent and its nearest subjet axis (∆R):”
Introduction to Soft-Collinear Effective Theory (2014, background): “For this purpose “N-subjettiness” was introduced in [258].”
Jet-images — deep learning edition (2015, methodology): “In particular, we compare the DNNs to n -subjettiness [30] τ 21 = τ 2 /τ 1 , the jet mass, and the distance ∆ R between the two leading p T subjets.”
Analytic boosted boson discrimination (2015, background): “A factorization theorem for the N -subjettiness observable τ (β) 1,2 [63, 64, 95] measured on boosted Z jets was presented in Ref.”
Performance of jet substructure techniques for large-R jets in proton-proton collisions at $ \sqrt{s}=7 $ TeV using the ATLAS detector (2013, background): “One of the primary applications of N -subjettiness is as a discriminating variable in searches for highly boosted top quarks [18].”
A new method to distinguish hadronically decaying boosted Z bosons from W bosons using the ATLAS detector (2015, neutral): “The type-tagger variables are largely independent of typical boson-tagger discriminants liken-subjettiness [7], which rely on the two-prong hard structure of both the W and Z decays.”
Jet substructure at the Large Hadron Collider (2018, background): “While grooming techniques were found to improve the performance (higher background rejection at fixed signal efficiency) of the jet mass, N subjettiness ratios [110, 111] were found to perform better without grooming for boosted boson tagging [105].”
Looking Inside Jets (2019, background): “As the name suggests, N -subjettiness [140] is a jet shape that aims to discriminate jets according to the number N of subjets they are made of.”
Jet mass and substructure of inclusive jets in sqrt(s) = 7 TeV pp collisions with the ATLAS experiment (2012, methodology): “In addition to the mass, two sets of substructure variables, kt splitting scales [15] and N -subjettiness ratios [16], are measured.”
Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider (2019, background): “This strategy could be helpful in cases such as photonjets (dark showers that decay into SM photons), which can otherwise pose significant trigger challenges: substructure variables such as n-subjettiness [566] and energy-energy correlations can be very effective at separating QCD jets, photons and ”
Power counting to better jet observables (2014, methodology): “While we will mostly focus on the energy correlation functions, we will also discuss the N -subjettiness observables [54, 55] as a point of reference.”
Gaining (mutual) information about quark/gluon discrimination (2014, methodology): “To avoid any effects from recoil [9, 24–27], we identify jets using 1-jettiness [49, 50] as a jet finder [51], taking the winner-take-all axis [21–23] as the jet center.”
Boosted objects: a probe of beyond the standard model physics (2010, background): “Another promising new approach, known as subjettiness [ 84 ] was published only after the workshop.”