  In collider experiments, the kinematic reconstruction of heavy, short-lived
particles is vital for precision tests of the Standard Model and in searches
for physics beyond it. Performing kinematic reconstruction in collider events
with many final-state jets, such as the all-hadronic decay of topantitop quark
pairs, is challenging. We present HyPER, a graph neural network that uses
blended graph-hypergraph representation learning to reconstruct parent
particles from sets of final-state objects. HyPER is tested on simulation and
shown to perform favorably when compared to existing state-of-the-art
reconstruction techniques, while demonstrating superior parameter efficiency.
The novel hypergraph approach allows the method to be applied to particle
reconstruction in a multitude of different physics processes.
