Learning Standard Model structure from LHC data with Riemannian flow matching
By Midori Kato · Paper · hep-ph
In this work we demonstrate that a single transformer-based generative model can capture Standard Model structure spanning five decades of invariant mass, from the sub-GeV regime to the TeV continuum, a range that no single Monte Carlo sample covers. To achieve this we design \te