https://doi.org/10.1140/epjc/s10052-025-14443-z
Regular Article - Experimental Physics
HGPflow: extending hypergraph particle flow to collider event reconstruction
1
Weizmann Institute of Science, Rehovot, Israel
2
INFN and University of Genova, Genoa, Italy
3
Technical University of Munich, Munich, Germany
4
Max Planck Institute for Physics, Munich, Germany
a
nilotpal.kakati@weizmann.ac.il
Received:
16
January
2025
Accepted:
17
June
2025
Published online:
6
August
2025
In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been explored in the context of single jets. In this paper, we expand the scope to full proton–proton and electron–positron collision events and study reconstruction quality using metrics at the particle, jet, and event levels. Instead of passing entire events through HGPflow, we train it on smaller partitions for scalability and to avoid potential bias from long-range correlations related to the physics process. We demonstrate that this approach is feasible and that on most metrics, HGPflow outperforms both traditional particle flow algorithms and a machine learning-based benchmark model.
© The Author(s) 2025
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Funded by SCOAP3.
