A rotation-equivariant graph neural network for learning hadronic SMEFT effects

TL;DR

This paper presents a rotation-equivariant graph neural network designed to analyze jet radiation patterns in LHC data, enabling more sensitive detection of SMEFT effects by decoupling angular orientation from jet substructure.

Contribution

The paper introduces a novel rotation-equivariant GNN architecture that is infrared- and collinear-safe, specifically tailored for SMEFT studies in high-energy physics.

Findings

Demonstrates robustness of the approach with toy studies.

Shows potential for future SMEFT probes at the LHC.

Effective in extracting angular information from jet data.

Abstract

We introduce a graph neural network architecture designed to extract novel phenomena in the Standard Model Effective Field Theory (SMEFT) context from LHC collision data. The proposed infrared- and collinear-safe architecture is sensitive to the angular orientation of radiation patterns in jets from hadronic decays of highly energetic massive particles. Equivariance with respect to rotations around the jet axis allows for extracting the information on the angular orientation decoupled from the jet substructure. We demonstrate the robustness of the approach and its potential for future probes of the SMEFT at the LHC through toy studies and with realistic event simulations of the WZ process in the semileptonic decay channel.