Hypergraphs in LHC phenomenology — the next frontier of IRC-safe feature extraction

Konar, Partha; Ngairangbam, Vishal S.; Spannowsky, Michael

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.

Citation

Konar, P., Ngairangbam, V. S., & Spannowsky, M. (2024). Hypergraphs in LHC phenomenology — the next frontier of IRC-safe feature extraction. Journal of High Energy Physics, 2024(1), Article 113. https://doi.org/10.1007/jhep01%282024%29113

• Journal Article Type: Article
• Acceptance Date: Dec 31, 2023
• Online Publication Date: Jan 19, 2024
• Publication Date: 2024
• Deposit Date: Mar 26, 2024
• Publicly Available Date: Mar 26, 2024
• Journal: Journal of High Energy Physics
• Print ISSN: 1126-6708
• Electronic ISSN: 1029-8479
• Publisher: Scuola Internazionale Superiore di Studi Avanzati (SISSA)
• Peer Reviewed: Peer Reviewed
• Volume: 2024
• Issue: 1
• Article Number: 113
• DOI: https://doi.org/10.1007/jhep01%282024%29113
• Public URL: https://durham-repository.worktribe.com/output/2167390