Hypergraphs in LHC Phenomenology -- The Next Frontier of IRC-Safe Feature Extraction

Abstract

In this study, we critically evaluate the approximation capabilities of existing infra-red and collinear (IRC) safe feature extraction algorithms, namely Energy Flow Networks (EFNs) and Energy-weighted Message Passing Networks (EMPNs). Our analysis reveals that these algorithms fall short in extracting features from any $N$-point correlation that isn't a power of two, based on the complete basis of IRC safe observables, specifically C-correlators. To address this limitation, we introduce the Hypergraph Energy-weighted Message Passing Networks (H-EMPNs), designed to capture any $N$-point correlation among particles efficiently. Using the case study of top vs. QCD jets, which holds significant information in its 3-point correlations, we demonstrate that H-EMPNs targeting up to N=3 correlations exhibit superior performance compared to EMPNs focusing on up to N=4 correlations within jet constituents.

Minor modifications in text and figure. Matches published version

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Cited by in corpus (6)

• Equivariant, Safe and Sensitive -- Graph Networks for New Physics
• Safe but Incalculable: Energy-weighting is not all you need
• Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects
• Foundations of automatic feature extraction at LHC--point clouds and graphs
• Reconstructing short-lived particles using hypergraph representation learning
• Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction