  In collider experiments, an event is characterized by two distinct yet
mutually complementary features: the `global features' and the `local
features'. Kinematic information such as the event topology of a hard process,
masses, and spins of particles comprises global features spanning the entire
phase space. This global feature can be inferred from reconstructed objects. In
contrast, representations of particles in gauge groups, such as Quantum
Chromodynamics (QCD), offer localized features revealing the dynamics of an
underlying theory. These local features, particularly observed in the patterns
of radiation as raw data in various detector components, complement the global
kinematic features. In this letter, we propose a simple but effective neural
network architecture that seamlessly integrates information from both
kinematics and QCD to enhance the signal sensitivity at colliders.
