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

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