Energy flow polynomials: a complete linear basis for jet substructure
By Patrick T. Komiske, E. Metodiev, Jesse Thaler. 2017
158 citations recorded by the source corpus.
A bstractWe introduce the energy flow polynomials: a complete set of jet substructure observables which form a discrete linear basis for all infrared- and collinear-safe observables. Energy flow polynomials are multiparticle energy correlators with specific angular structures that are a direct consequence of infrared and collinear safety. We establish a powerful graph-theoretic representation of the energy flow polynomials which allows us to design efficient algorithms for their computation. Many common jet observables are exact linear combinations of energy flow polynomials, and we demonstrate the linear spanning nature of the energy flow basis by performing regression for several common jet observables. Using linear classification with energy flow polynomials, we achieve excellent performance on three representative jet tagging problems: quark/gluon discrimination, boosted W tagging, and boosted top tagging. The energy flow basis provides a systematic framework for complete investigations of jet substructure using linear methods.
Subfields: Nuclear and High Energy Physics
View Paper: https://www.semanticscholar.org/paper/e1c0ef0b098a3c5af147646e649080ef15b12358
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In Nuclear and High Energy Physics
- A. Larkoski
- Benjamin Nachman
- I. Moult
- Jesse Thaler
- G. Soyez
- E. Metodiev
- Patrick T. Komiske
- Cms Collaboration
Citations in the corpus
- Energy flow networks: deep sets for particle jets (2018, methodology): “For instance, hadronic jet classification has been thoroughly studied using low-level [4–23] and high-level [24–29] input observables.”
- Machine learning and the physical sciences (2019, methodology): “Similarly, networks have been developed motivated by invariance to permutations on the particles presented to the network and stability to details of the radiation pattern of particles, (Komiske et al., 2018b, 2019).”
- Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, background): “One such approach is the energy flow polynomials [231], which are a basis of functions that provably span IRC-safe jet observables(4).”
- Looking Inside Jets (2019, methodology): “Several examples of this have appeared very recently — like QCD-aware networks [257], energy-flow polynomials and networks [146, 147] or the Lund jet plane [65] — and we should definitely expect more in the future.”