QCD splittings are among the most fundamental theory concepts at the LHC. We show how they can be studied systematically with the help of invertible neural networks. These networks work with sub-jet information to extract fundamental parameters from jet samples. Our approach expands the LEP measurements of QCD Casimirs to a systematic test of QCD properties based on low-level jet observables. Starting with an toy example we study the effect of the full shower, hadronization, and detector effects in detail.
Cited by 1
Bellagente et al., Understanding Event-Generation Networks via Uncertainties
SciPost Phys. 13, 003 (2022) [Crossref]
Authors / Affiliations: mappings to Contributors and OrganizationsSee all Organizations.
- 1 Sebastian Bieringer,
- 1 Anja Butter,
- 1 Theo Heimel,
- 2 Stefan Höche,
- 1 Ullrich Köthe,
- 1 Tilman Plehn,
- 1 Stefan T. Radev
- 1 Ruprecht-Karls-Universität Heidelberg / Heidelberg University
- 2 Fermi National Accelerator Laboratory [Fermilab]