Speaker
Description
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. We introduce 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
$\Phi_{N+1} = \Phi_{N} \times \Phi_{1}$
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. We demonstrate the approach on
Drell-Yan and $gg \to t\overline{t} + \text{gluons}$
processes. Because the augmentation is used only to initialise the proposal
distribution, exact event weighting preserves unbiased Monte Carlo estimates
of physical observables.