14443 10.1140/epjc/s10052-025-14443-z 847 29 HGPflow: extending hypergraph particle flow to collider event reconstruction Regular Article - Experimental Physics Experimental Physics 1 18 2025 6 20 2025 1 16 2025 6 17 2025 8 6 Weizmann Institute and MBZUAI Collaboration Grant BSF-NSF Grant 2020780 Deutsche Forschungs- gemeinschaft Excellence Cluster ORIGINS EXC-2094-390783311 The Author(s) 2025 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . Funded by SCOAP3. 10052 85 85 8 8 Nilotpal Kakati nilotpal.kakati@weizmann.ac.il Etienne Dreyer Anna Ivina Francesco Armando Di Bello Lukas Heinrich Marumi Kado Eilam Gross https://ror.org/0316ej306 grid.13992.30 0000 0004 0604 7563 Weizmann Institute of Science Rehovot Israel https://ror.org/0107c5v14 grid.5606.5 0000 0001 2151 3065 INFN and University of Genova Genoa Italy https://ror.org/02kkvpp62 grid.6936.a 0000000123222966 Technical University of Munich Munich Germany https://ror.org/0079jjr10 grid.435824.c 0000 0001 2375 0603 Max Planck Institute for Physics Munich Germany Abstract 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.