Soft drop

By A. Larkoski, S. Marzani, G. Soyez, Jesse Thaler. 2014

783 citations recorded by the source corpus.

A bstractWe introduce a new jet substructure technique called “soft drop declustering”, which recursively removes soft wide-angle radiation from a jet. The soft drop algorithm depends on two parameters — a soft threshold zcut and an angular exponent β — with the β = 0 limit corresponding roughly to the (modified) mass drop procedure. To gain an analytic understanding of soft drop and highlight the β dependence, we perform resummed calculations for three observables on soft-dropped jets: the energy correlation functions, the groomed jet radius, and the energy loss due to soft drop. The β = 0 limit of the energy loss is particularly interesting, since it is not only “Sudakov safe” but also largely insensitive to the value of the strong coupling constant. While our calculations are strictly accurate only to modified leading-logarithmic order, we also include a discussion of higher-order effects such as multiple emissions and (the absence of) non-global logarithms. We compare our analytic results to parton shower simulations and find good agreement, and we also estimate the impact of non-perturbative effects such as hadronization and the underlying event. Finally, we demonstrate how soft drop can be used for tagging boosted W bosons, and we speculate on the potential advantages of using soft drop for pileup mitigation.

Subfields: Artificial Intelligence, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/2b2be44cb6c54c5cffd3991a6efc75ad7bf8e4b8

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Citations in the corpus

The Lund jet plane (2018, methodology): “025, which corresponds to the emission that would be selected by the mMDT tagger [19] with the same zcut or equivalently by the Soft-Drop (SD) procedure [64] with β = 0 and that zcut.”
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, methodology): “[335] introduced the soft drop grooming algorithm as a generalization of the mMDT groomer.”
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].”
Les Houches 2015: Physics at TeV Colliders Standard Model Working Group Report (2016, background): “That said, because techniques like soft drop [486] remove soft radiation from jets, they tend to reduce the process dependence in quark/gluon radiation patterns, and may therefore yield a more robust theoretical definition for quark and gluon jets.”
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques (2020, methodology): “The grooming method used most often in CMS is the “modified mass drop tagger” algorithm (mMDT) [58], which is a special case of the “soft drop” (SD) method [59].”
Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV (2017, methodology): “In this paper, the soft-drop algorithm [45, 46], which recursively removes soft wide-angle radiation from a jet, is used to resolve the substructure of the AK8 jets.”
Gaining (mutual) information about quark/gluon discrimination (2014, background): “One option is to do quark/gluon tagging in concert with soft drop declustering [36] (a generalization of modified mass drop tagging [34, 72]).”
ParticleNet: Jet Tagging via Particle Clouds (2019, methodology): “Methods based on the QCD theory have been proposed and continuously improved for discriminating quark and gluon jets [1–7], tagging jets originating from high-momentum heavy particles [8–18], etc.”
Jet substructure at the Large Hadron Collider (2018, methodology): “The three main algorithms used by ATLAS and CMS are trimming [90], pruning [91], and soft drop [92].”
Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV (2017, methodology): “In this paper, the soft-drop algorithm [45, 46], which recursively removes soft wide-angle radiation from a jet, is used to resolve the substructure of the AK8 jets.”
Medium response in JEWEL and its impact on jet shape observables in heavy ion collisions (2017, methodology): “In p+p collisions, this method has been studied in some detail [49, 51], but in heavy ion collisions the exact meaning of the grooming procedure is not obvious, due to the presence of the fluctuating underlying heavy ion event and the increased soft sector, that the procedure tries to remove.”
Pileup mitigation at CMS in 13 TeV data (2020, methodology): “The main grooming algorithm used in CMS is the soft drop or modified mass drop tagger [36, 37].”
Energy flow networks: deep sets for particle jets (2018, background): “Numerous individual observables have been proposed to study jets including the jet mass, constituent multiplicity, image activity [69], N -subjettiness [70, 71], track-based observables [72, 73], generalized angularities [74], (generalized) energy correlation functions [75, 76], soft drop multiplici”
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.”
Playing tag with ANN: boosted top identification with pattern recognition (2015, methodology): “Soft drop declustering (a generalization of modiﬁed mass drop tagging) is another method which has been recently developed for removing non-global contributions (soft radiation) to the jet [34].”
Analytic boosted boson discrimination (2015, background): “This ensures that the only logarithms which appear in a given soft function that are sensitive to both in-jet and out-of-jet scales are true non-global logarithms (NGLs) [78], which first appear at two-loop order in the calculation of a particular soft function.6 Here we focus on the refactorization”
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.”