Measuring QCD Splittings with Invertible Networks

TL;DR

This paper introduces a novel method using invertible neural networks to analyze QCD splittings from jet data, enabling systematic testing of QCD properties with low-level observables at the LHC.

Contribution

It presents a new approach employing invertible neural networks to extract fundamental QCD parameters from jet substructure data.

Findings

Successfully modeled the effect of shower, hadronization, and detector effects.

Extended LEP QCD measurements to LHC jet observables.

Demonstrated systematic analysis of QCD splittings using neural networks.

Abstract

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.