TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction

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 multi-task learning, enabling simultaneous event reconstruction and classification. TIGER thus provides a powerful and generalizable tool for physics analysis at the LHC.

16 pages, 3 figures, 2 tables

References in corpus (20)

• The anti-k_t jet clustering algorithm
• A Brief Introduction to PYTHIA 8.1
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• MadGraph 5 : Going Beyond
• A likelihood-based reconstruction algorithm for top-quark pairs and the KLFitter framework
• Top-quark mass measurement in the all-hadronic $t\bar{t}$ decay channel at $\sqrt{s}$ = 8 TeV with the ATLAS detector
• SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention
• Measurements of top-quark pair single- and double-differential cross-sections in the all-hadronic channel in $pp$ collisions at $\sqrt{s}=13~\mbox{TeV}$ using the ATLAS detector
• Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
• Combination of searches for Higgs boson pair production in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Reconstructing particles in jets using set transformer and hypergraph prediction networks
• Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC
• Topological Reconstruction of Particle Physics Processes using Graph Neural Networks
• Machine Learning for Particle Flow Reconstruction at CMS
• A Lorentz-Equivariant Transformer for All of the LHC
• Measurements of $WH$ and $ZH$ production with Higgs boson decays into bottom quarks and direct constraints on the charm Yukawa coupling in $13\mathrm{TeV}$ $pp$ collisions with the ATLAS detector
• Reconstruction of Unstable Heavy Particles Using Deep Symmetry-Preserving Attention Networks
• CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry
• Reconstructing short-lived particles using hypergraph representation learning
• Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks