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  • Open Access

Topology-agnostic, hierarchical graph network for event reconstruction

Nathalie Soybelman*, Nilotpal Kakati, and Eilam Gross

Francesco A. Di Bello†

  • *Contact author: nathalie.soybelman@weizmann.ac.il
  • †Present address: University of Pisa, Italy.
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Phys. Rev. D 113, 012014 – Published 26 January, 2026

DOI: https://doi.org/10.1103/z57h-y3rx

Abstract

Event reconstruction at the LHC, the task of assigning observed physics objects to their true origins, is a central challenge for precision measurements and searches. Many existing machine learning approaches address this problem but rely on a single event topology, restricting their applicability to realistic analyses where multiple signal and background processes with different structures are present. To overcome this, we present TIGER, a novel hierarchical graph network that is fundamentally topology agnostic. By incorporating only the common underlying structure of sequential two-body decays, our model can reconstruct complex events without process-specific assumptions. This flexible architecture supports multitask learning, enabling simultaneous event reconstruction and classification. TIGER thus provides a powerful and generalizable tool for physics analysis at the LHC.

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Phys. Rev. D 113, 012014– Published 26 January, 2026
  • Received 28 October 2025
  • Accepted 1 December 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

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