Measuring QCD splittings with invertible networks

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.

Automated summary

Researchers systematically studied QCD splittings, a fundamental theory concept at the LHC, using invertible neural networks to extract parameters from jet samples based on sub-jet information. They expanded LEP measurements of QCD Casimirs to systematically test QCD properties using low-level jet observables, studying the effects of the full shower, hadronization, and detector effects in detail through a toy example.