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Abstract |
Event generation for the LHC can be supplemented by generative
adversarial networks, which generate physical events and avoid highly
inefficient event unweighting. For top pair production we show how such
a network describes intermediate on-shell particles, phase space
boundaries, and tails of distributions. In particular, we introduce the
maximum mean discrepancy to resolve sharp local features.
The generative network can be extended to perform addition and
subtraction of event samples, a common problem in LHC simulations. We
show how generative adversarial networks can produce new event samples
with a phase space distribution corresponding to added or subtracted
input samples. We illustrate its performance for the subtraction of the
photon continuum from the complete Drell–Yan process. |
