Equivariant, safe and sensitive — graph networks for new physics

Author:

Bhardwaj AkankshaORCID,Englert ChristophORCID,Naskar WrishikORCID,Ngairangbam Vishal S.ORCID,Spannowsky MichaelORCID

Abstract

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.

Publisher

Springer Science and Business Media LLC

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Foundations of automatic feature extraction at LHC–point clouds and graphs;The European Physical Journal Special Topics;2024-09-11

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