Dissecting Soft Radiation with Factorization

TL;DR

This paper uses a QCD factorization theorem to analyze soft radiation effects in high-energy collisions, revealing universal properties and dependencies that help distinguish different soft activity sources.

Contribution

It provides a novel factorization-based framework to dissect soft radiation contributions, including nonperturbative effects and initial state radiation, with universal and process-dependent insights.

Findings

Nonperturbative contributions involve only odd powers of jet radius R.

Linear R term is universal for quark and gluon jets.

Soft initial state radiation depends on the initial partons' color state.

Abstract

An essential part of high-energy hadronic collisions is the soft hadronic activity that underlies the primary hard interaction. It includes soft radiation from the primary hard partons, secondary multiple parton interactions (MPI), and factorization-violating effects. The invariant mass spectrum of the leading jet in $Z$+jet and $H$+jet events is directly sensitive to these effects, and we use a QCD factorization theorem to predict its dependence on the jet radius $R$, jet $p_T$, jet rapidity, and partonic process for both the perturbative and nonperturbative components of primary soft radiation. We prove that the nonperturbative contributions involve only odd powers of $R$, and the linear $R$ term is universal for quark and gluon jets. The hadronization model in PYTHIA8 agrees well with these properties. The perturbative soft initial state radiation (ISR) has a contribution that depends on the jet area in the same way as the underlying event, but this degeneracy is broken by dependence on the jet $p_T$. The size of this soft ISR contribution is proportional to the color state of the initial partons, yielding the same positive contribution for $gg\to Hg$ and $gq\to Zq$, but a negative interference contribution for $q\bar q\to Z g$. Hence, measuring these dependencies allows one to separate hadronization, soft ISR, and MPI contributions in the data.