https://doi.org/10.1140/epjc/s10052-023-11677-7
Regular Article - Experimental Physics
Reconstructing particles in jets using set transformer and hypergraph prediction networks
1
INFN and University of Genova, Genoa, Italy
2
Weizmann Institute of Science, Rehovot, Israel
3
ICEPP, University of Tokyo, Tokyo, Japan
4
Technical University of Munich, Munich, Germany
5
Max Planck Institute for Physics, Munich, Germany
6
INFN and Sapienza University of Rome, Rome, Italy
h
nilotpal.kakati@weizmann.ac.il
Received:
11
December
2022
Accepted:
4
June
2023
Published online:
11
July
2023
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.
© The Author(s) 2023
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Funded by SCOAP3. SCOAP3 supports the goals of the International Year of Basic Sciences for Sustainable Development.
