Topology-agnostic, hierarchical graph network for event reconstruction
Year: 2025
Type: article
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.
DOI: 10.1103/z57h-y3rx
Authors: Nathalie Soybelman, Nilotpal Kakati, Eilam Gross, Francesco A. Di Bello
Institutions: Weizmann Institute of Science, University of Genoa
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Awards: G3162942938, G700315086
OpenAlex ID: W4417212711