ShellFlow

ShellFlow: Transformer-based generative model learns Standard Model structure from LHC collision data

Researchers introduce ShellFlow, a Riemannian conditional flow matching model built on a transformer backbone, trained on ~1 billion real proton-proton collision events from the ATLAS Open Data 13 TeV release. With only on-shell kinematics as physics priors, the model autonomously recovers particle resonances (J/ψ, Υ, Z), the leptonic Weinberg angle, and W and top-quark masses across five decades of invariant mass. The result demonstrates that a substantial fraction of Standard Model structure is learnable directly from raw LHC data without Monte Carlo simulation or explicit physics supervision.