Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and
  Trajectory Displacement Information
Joschka Birk Erik Buhmann Cedric Ewen Gregor Kasieczka David Shih
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.