  The task of reconstructing particles from low-level detector response data to
predict the set of final state particles in collision events represents a
set-to-set prediction task requiring the use of multiple features and their
correlations in the input data. We deploy three separate set-to-set neural
network architectures to reconstruct particles in events containing a single
jet in a fully-simulated calorimeter. Performance is evaluated in terms of
particle reconstruction quality, properties regression, and jet-level metrics.
The results demonstrate that such a high dimensional end-to-end approach
succeeds in surpassing basic parametric approaches in disentangling individual
neutral particles inside of jets and optimizing the use of complementary
detector information. In particular, the performance comparison favors a novel
architecture based on learning hypergraph structure, HGPflow, which benefits
from a physically-interpretable approach to particle reconstruction.
