Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information

TL;DR

This paper presents a permutation-equivariant continuous normalizing flow model trained on the JetClass dataset, capable of generating detailed jet constituents including particle-ID and trajectory information, surpassing traditional kinematic-only models.

Contribution

It introduces the first generative model that incorporates particle-ID and trajectory displacement features for jet generation, trained with flow matching on the JetClass dataset.

Findings

Accurately models additional jet features beyond kinematics.

Generates diverse jet types with a single conditioned model.

Enhances jet simulation realism for high-energy physics.

Abstract

We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. It is conditioned on the jet type, so that a single model can be used to generate the ten different jet types of JetClass. For the first time, we also introduce a generative model that goes beyond the kinematic features of jet constituents. The JetClass dataset includes more features, such as particle-ID and track impact parameter, and we demonstrate that our CNF can accurately model all of these additional features as well. Our generative model for JetClass expands on the versatility of existing jet generation techniques, enhancing their potential utility in high-energy physics research, and offering a more comprehensive understanding of the generated jets.