  The high-luminosity upgrade of the LHC will come with unprecedented physics
and computing challenges. One of these challenges is the accurate
reconstruction of particles in events with up to 200 simultaneous proton-proton
interactions. The planned CMS High Granularity Calorimeter offers fine spatial
resolution for this purpose, with more than 6 million channels, but also poses
unique challenges to reconstruction algorithms aiming to reconstruct individual
particle showers. In this contribution, we propose an end-to-end
machine-learning method that performs clustering, classification, and energy
and position regression in one step while staying within memory and
computational constraints. We employ GravNet, a graph neural network, and an
object condensation loss function to achieve this task. Additionally, we
propose a method to relate truth showers to reconstructed showers by maximising
the energy weighted intersection over union using maximal weight matching. Our
results show the efficiency of our method and highlight a promising research
direction to be investigated further.
