Maximizing boosted top identification by minimizing N-subjettiness

By Jesse Thaler, K. Tilburg. Journal of High Energy Physics, 2011

453 citations recorded by the source corpus.

N -subjettiness is a jet shape designed to identify boosted hadronic objects such as top quarks. Given N subjet axes within a jet, N-subjettiness sums the angular distances of jet constituents to their nearest subjet axis. Here, we generalize and improve on N -subjettiness by minimizing over all possible subjet directions, using a new variant of the k-means clustering algorithm. On boosted top benchmark samples from the BOOST2010 workshop, we demonstrate that a simple cut on the 3-subjettiness to 2-subjettiness ratio yields 20% (50%) tagging efficiency for a 0.23% (4.1%) fake rate, making N -subjettiness a highly effective boosted top tagger. N-subjettiness can be modified by adjusting an angular weighting exponent, and we find that the jet broadening measure is preferred for boosted top searches. We also explore multivariate techniques, and show that additional improvements are possible using a modified Fisher discriminant. Finally, we briefly mention how our minimization procedure can be extended to the entire event, allowing the event shape N-jettiness to act as a fixed N cone jet algorithm.

Subfields: Artificial Intelligence, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/5fc4c556d51a515fe1cd796bea6b14fc43f1c028

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Citations in the corpus

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].”
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.”
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, unknown): “[227, 228, 229] called N subjettiness.”
Beyond the Standard Model Physics at the HL-LHC and HE-LHC (2018, methodology): “For top quarks with a large boost (transverse momentum, pT , greater than 400 GeV), an identification algorithm based on the softdrop [504] jet grooming algorithm is used in combination with N -subjettiness [508] and subjet b-tagging algorithms to identify the decay of the top quark with no leptons.”
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): “J H E P 0 9 ( 2 0 1 3 ) 0 7 6 N-subjettiness: the N -subjettiness variables τN [18, 51] are observables related to the subjet multiplicity.”
Looking Inside Jets (2019, unknown): “[142] starting with a set of seeds.”
Les Houches 2017: Physics at TeV Colliders Standard Model Working Group Report (2017, methodology): “The N -subjettiness observables are defined as [798,799,815]16”
Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd–27th of July 2012 (2013, background): “– N -subjettiness [75,76] measures how much of a jet’s radiation is aligned along N subjet axes in the plane formed by the rapidity y and azimuthal angle φ.”
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): “In subsequent work [56], Thaler and van Tilburg showed that the initial step of choosing candidate subjet axes is in fact unnecessary.”
Jet-images: computer vision inspired techniques for jet tagging (2014, methodology): “D likelihood ratios of the output distribution of the FLD for W-jets and QCD jets, can be seen in ﬁgure 3a, along with the rejection vs. eﬃciency curves observed when using N-subjettiness ( τ 2 /τ 1 ) [7, 8] computed analogously with the 1-”
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”
Higgs Physics at the HL-LHC and HE-LHC (2019, methodology): “A selection is applied on the N-sub-jettiness variable [293] to reduce the background contamination, mostly represented by di-jet production from QCD interactions.”
Energy flow polynomials: a complete linear basis for jet substructure (2017, methodology): “12), and the N -subjettiness observables [54, 55] are calculated using kT axes.”
Learning to Pivot with Adversarial Networks (2016, methodology): “The features used in the classifiers are: trimmed jet invariant mass, N-subjettiness τ 21 [28, 29], and the energy correlation functions C 2 , C β=2 2 , D β=1 2 , D β=2 2 [24].”
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].”