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.
@article{arxiv.2402.12449,
title = {Equivariant, Safe and Sensitive -- Graph Networks for New Physics},
author = {Akanksha Bhardwaj and Christoph Englert and Wrishik Naskar and Vishal S. Ngairangbam and Michael Spannowsky},
journal= {arXiv preprint arXiv:2402.12449},
year = {2024}
}
Comments
Restructured discussions in section 2.1 for better clarity. Matches published version