Recombination algorithms and jet substructure: Pruning as a tool for heavy particle searches

By S. D. Ellis, C. Vermilion, Jonathan R. Walsh. 2009

418 citations recorded by the source corpus.

We discuss jet substructure in recombination algorithms for QCD jets and single jets from heavy particle decays. We demonstrate that the jet algorithm can introduce significant systematic effects into the substructure. By characterizing these systematic effects and the substructure from QCD, splash-in, and heavy particle decays, we identify a technique, pruning, to better identify heavy particle decays into single jets and distinguish them from QCD jets. Pruning removes protojets typical of soft, wide-angle radiation, improves the mass resolution of jets reconstructing heavy particle decays, and decreases the QCD background to these decays. We show that pruning provides significant improvements over unpruned jets in identifying top quarks and $W$ bosons and separating them from a QCD background, and may be useful in a search for heavy particles.

Subfields: Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/6c01092d39356ae3b5b019cfa0ed8946ea6fae7c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Nuclear and High Energy Physics

Citations in the corpus

Boosted objects: a probe of beyond the standard model physics (2010, methodology): “Pruning was introduced by Ellis, Vermilion and Walsh [64, 65].”
Power counting to better jet observables (2014, methodology): “Several methods for jet grooming and pile-up subtraction have been presented [47, 73, 81–88], and are used by the experiments [6, 16–18, 21], but we will not consider them here.”
Maximizing boosted top identification by minimizing N-subjettiness (2011, neutral): “The five algorithms shown in figure 1 are referred to as “Hopkins” [19], “CMS” [58–60], “Pruning” [27, 28], “ATLAS” [17, 61, 62], and “Thaler/Wang” [18].”
Jet shapes and jet algorithms in SCET (2010, background): “Recently, several groups have explored strategies to probe jet substructure to distinguish jets produced by light partons in QCD from those produced by heavier particles [1, 2, 3, 4, 5, 6, 7, 8], and methods to “clean” jets of soft radiation to more easily identify their origin, such as “filtering” ”
Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure (2016, background): “1, jet pruning [8, 9], and soft drop [12] with zcut = 0.”
Performance of jet substructure techniques for large-R jets in proton-proton collisions at $ \sqrt{s}=7 $ TeV using the ATLAS detector (2013, methodology): “Pruning: the pruning algorithm [3, 11] is similar to trimming in that it removes constituents with a small relative pT, but it additionally applies a veto on wide-angle radiation.”
Pileup per particle identification (2014, background): “filtering [13], pruning [14, 15], trimming [16], soft drop [17]): systematically discards a subset of a jet’s constituents that are believed to obscure the signal process.”
Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV (2017, methodology): “In this paper, jet pruning [51] is applied to the AK8 jets.”
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): “Filtering, pruning, and trimming are jet grooming algorithms that modify the substructure of the jet to remove contributions from the underlying event and pileup [35, 33, 34, 65].”
Identification techniques for highly boosted W bosons that decay into hadrons (2014, methodology): “For this reason, the use of jet grooming methods such as filtering [48], trimming [49], or pruning [50, 51], improves discrimination by removing the softer radiation, as this shifts the jet mass of QCD jets to smaller values, while maintaining the jet mass for W jets close to the W mass.”
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].”
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, background): “Among the early jet groomers that have seen wide use experimentally are filtering [42], trimming [332], and pruning [333].”
Jet-images: computer vision inspired techniques for jet tagging (2014, background): “…includes, but is not limited to, subjet ﬁnding and jet tagging / jet ﬂavor identiﬁcation algorithms [2–12], jet grooming and pileup mitigation [13–15], template and matrix element techniques [16, 17], energy ﬂow and event shapes [18–24], and probabilistic jet clustering [25, 26]; more inclusive…”