Factorization for groomed jet substructure beyond the next-to-leading logarithm

By C. Frye, A. Larkoski, M. Schwartz, K. Yan. Journal of High Energy Physics, 2016

162 citations recorded by the source corpus.

Jet grooming algorithms are widely used in experimental analyses at hadron colliders to remove contaminating radiation from within jets. While the algorithms perform a great service to the experiments, their intricate algorithmic structure and multiple parameters has frustrated precision theoretic understanding. In this paper, we demonstrate that one particular groomer called soft drop actually makes precision jet substructure easier. In particular, we derive a factorization formula for a large class of soft drop jet substructure observables, including jet mass. The essential observation that allows for this factorization is that, without the soft wide-angle radiation groomed by soft drop, all singular contributions are collinear. The simplicity and universality of the collinear limit in QCD allows us to show that to all orders, the normalized differential cross section has no contributions from non-global logarithms. It is also independent of process, up to the relative fraction of quark and gluon jets. In fact, soft drop allows us to define this fraction precisely. The factorization theorem also explains why soft drop observables are less sensitive to hadronization than their ungroomed counterparts. Using the factorization theorem, we resum the soft drop jet mass to next-to-next-to-leading logarithmic accuracy. This requires calculating some clustering effects that are closely related to corresponding effects found in jet veto calculations. We match our resummed calculation to fixed order results for both e+e− → dijets and pp → Z + j events, producing the first jet substructure predictions (groomed or ungroomed) to this accuracy for the LHC.

Subfields: Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/7685ba30d751b7e00b620ea09f39556655dc3e09

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Citations in the corpus

Deep learning in color: towards automated quark/gluon jet discrimination (2016, background): “in [28] it was shown that soft-drop allows for an unambiguous quark/gluon jet definition).”
Systematics of quark/gluon tagging (2017, background): “One advantage of working with groomed samples is that jet grooming reduces the process dependence in quark/gluon radiation patterns [59, 60].”
Energy flow polynomials: a complete linear basis for jet substructure (2017, methodology): “Alternatively, one standard tool that is used to mitigate non-perturbative effects is jet grooming [5, 105–109], which also simplifies first-principles calculations and allows for “quark” and “gluon” jets to be theoretically well-defined [139].”
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, methodology): “We also emphasize that there has been significant re- cent work on improving the perturbative description of the parton shower, including a recent implementation of a 2 → 4 NNLL shower [154], and triple collinear splitting functions [155, 156].”
Classification without labels: learning from mixed samples in high energy physics (2017, background): “Jet grooming techniques [54–59] can mitigate the impact of soft effects to provide a more universal “quark” jet definition [60, 61].”
Les Houches 2017: Physics at TeV Colliders Standard Model Working Group Report (2017, methodology): “The two sets of predictions for dijets thus far have been matched to LO [646, 647] and NLO [648, 649] and have used hadronization models to study NP corrections [648,649].”
Jet substructure at the Large Hadron Collider (2018, unknown): “[128].”
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques (2020, background): “This algorithm systematically removes the soft and collinear radiation from the jet in a manner that can be theoretically calculated [60, 61] (comparisons to data are found in ref.”