Abstract
We 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 $z_\text{cut}$ and an angular exponent $\beta$--with the $\beta = 0$ limit corresponding roughly to the (modified) mass drop procedure. To gain an analytic understanding of soft drop and highlight the $\beta$ 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 $\beta = 0$ 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.
Highlights
We introduce a new jet substructure technique called “soft drop declustering”, which recursively removes soft wide-angle radiation from a jet
We investigate a simple approximation to the all-order C1(α) distribution by working to modified leading logarithmic (MLL) accuracy, i.e. we aim to capture the terms αsnL2n−q with q = 0, 1 in the expansion of the cumulative distribution Σ(C1(α)), which gives the probability for the observable to be less than a given value C1(α)
Soft drop generalizes the modified mass drop tagger (mMDT) procedure by incorporating an angular exponent β, and simplifies mMDT by removing the mass drop condition
Summary
The study of jet substructure has significantly matured over the past five years [1,2,3], with numerous techniques proposed to tag boosted objects [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46], distinguish quark from gluon jets [44, 47,48,49,50,51], and mitigate the effects of jet contamination [6, 52,53,54,55,56,57,58,59,60,61]. One of the motivations for introducing the generalized soft drop procedure with β > 0 is to have a method (in the same spirit of trimming [53]) that gives IRC safe distributions for any (otherwise) IRC safe observable measured on groomed jets. While the focus of this paper is on the analytic properties of the soft drop procedure, we will cross check our results using parton shower Monte Carlo simulations
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