  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.
