Reconstructing particles in jets using set transformer and hypergraph prediction networks
F. A. Di Bello, E. Dreyer, S. Ganguly, E. Gross, L. Heinrich, A. Ivina, M. Kado, N. Kakati, L. Santi, J. Shlomi, Matteo Tusoni
Abstract
Abstract 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.