Equivariant, safe and sensitive — graph networks for new physics

Bhardwaj, Akanksha; Englert, Christoph; Naskar, Wrishik; Ngairangbam, Vishal S.; Spannowsky, Michael

Abstract

This study introduces a novel Graph Neural Network (GNN) architecture that leverages infrared and collinear (IRC) safety and equivariance to enhance the analysis of collider data for Beyond the Standard Model (BSM) discoveries. By integrating equivariance in the rapidity-azimuth plane with IRC-safe principles, our model significantly reduces computational overhead while ensuring theoretical consistency in identifying BSM scenarios amidst Quantum Chromodynamics backgrounds. The proposed GNN architecture demonstrates superior performance in tagging semi-visible jets, highlighting its potential as a robust tool for advancing BSM search strategies at high-energy colliders.

Citation

Bhardwaj, A., Englert, C., Naskar, W., Ngairangbam, V. S., & Spannowsky, M. (2024). Equivariant, safe and sensitive — graph networks for new physics. Journal of High Energy Physics, 2024(7), Article 245. https://doi.org/10.1007/jhep07%282024%29245

• Journal Article Type: Article
• Acceptance Date: Jul 4, 2024
• Online Publication Date: Jul 26, 2024
• Publication Date: 2024-07
• Deposit Date: Aug 6, 2024
• Publicly Available Date: Aug 6, 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: 7
• Article Number: 245
• DOI: https://doi.org/10.1007/jhep07%282024%29245
• Keywords: Jets and Jet Substructure, Dark Matter at Colliders
• Public URL: https://durham-repository.worktribe.com/output/2614763