
Benchmark datasets for LLoCa network evaluation on the partonic processes $q\bar q \to Z+ng$, generated using MadGraph5.The datasets zg, zgg, zggg include 10M events, while the dataset zgggg includes 100M events.Each data point contains the four-momenta of the scattering particles in a single row followed by the corresponding amplitude. We generate unweighted phase space points, then the squared scattering amplitudes are computed via a standalone MadGraph module. We apply global cuts to all final-state particles such that $p_T>20$ GeV and $\Delta R>0.4$. Originally generated for "Lorentz Local Canonicalization: How to make any Network Lorentz-equivariant", Spinner J. et al., arXiv:2505.20208.
collider physics, machine learning, LLoCa, multi-gluon final states, MadGraph, scattering amplitudes
collider physics, machine learning, LLoCa, multi-gluon final states, MadGraph, scattering amplitudes
