Too good to go: Upcycling Phase-Space Points for Multijet Processes

Accurate sampling of multi-particle phase spaces is a major bottleneck in high-energy-physics simulations at the LHC, especially for processes with many final-state particles where matrix-element evaluations become prohibitively expensive. This talk introduces a stacked training strategy for phase-space point generators that cuts the training cost dramatically while delivering improved sampling performance. This method exploits the nested structure of a factorised phase-space parametrisation 
Φ_{N+1} = Φ_N × Φ_1
property of the (N+1)-particle phase-space, so that a higher-multiplicity phase space can be constructed from a lower-multiplicity one, effectively transferring the knowledge of a low-dimensional sampler to a higher-dimensional one. The new training method is demonstrated using machine learning-based phase-space samplers for Drell-Yan and gg→tt+gluons processes. Because the augmentation is used only to initialise the proposal distribution, exact event weighting preserves unbiased Monte Carlo estimates of physical observables.