Hypergraphs in LHC Phenomenology -- The Next Frontier of IRC-Safe Feature Extraction
Partha Konar, Vishal S. Ngairangbam, and Michael Spannowsky

TL;DR
This paper identifies limitations in existing IRC-safe feature extraction algorithms for particle physics and introduces Hypergraph EMPNs, which effectively capture complex N-point correlations, improving jet classification performance.
Contribution
The paper proposes Hypergraph EMPNs that can extract all N-point correlations, overcoming the limitations of previous methods that only handle powers of two.
Findings
H-EMPNs outperform EMPNs in top vs. QCD jet classification.
H-EMPNs effectively capture 3-point correlations in jet data.
Existing algorithms are limited to N-point correlations that are powers of two.
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 -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 -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…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Particle Detector Development and Performance · Big Data and Digital Economy
