Convolved Substructure: Analytically Decorrelating Jet Substructure Observables

Abstract

A number of recent applications of jet substructure, in particular searches for light new particles, require substructure observables that are decorrelated with the jet mass. In this paper we introduce the Convolved SubStructure (CSS) approach, which uses a theoretical understanding of the observable to decorrelate the complete shape of its distribution. This decorrelation is performed by convolution with a shape function whose parameters and mass dependence are derived analytically. We consider in detail the case of the $D_2$ observable and perform an illustrative case study using a search for a light hadronically decaying $Z^{\prime }$. We find that the CSS approach completely decorrelates the $D_2$ observable over a wide range of masses. Our approach highlights the importance of improving the theoretical understanding of jet substructure observables to exploit increasingly subtle features for performance.

20 pages, 11 figures. v2. Corrected typo in legend in Figure 5. Updated Figure 11, minor modification to conclusions on discrimination power. v3. Updated to published version. Minor typos corrected

References in corpus (11)

• PYTHIA 6.4 Physics and Manual
• An Introduction to PYTHIA 8.2
• Power Counting to Better Jet Observables
• Momentum Flow Correlations from Event Shapes: Factorized Soft Gluons and Soft-Collinear Effective Theory
• New Angles on Energy Correlation Functions
• A generic anti-QCD jet tagger
• Designing Gapped Soft Functions for Jet Production
• Resummation of Double-Differential Cross Sections and Fully-Unintegrated Parton Distribution Functions
• Precision Jet Substructure from Boosted Event Shapes
• Dissecting Soft Radiation with Factorization
• Factorization and Resummation for Groomed Multi-Prong Jet Shapes

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