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2019

How to GAN LHC Events

Butter, Anja, Plehn, Tilman, Winterhalder, Ramon

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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.
It can be extended in a straightforward manner to include for instance off-shell contributions, higher orders, or approximate detector effects.

How to GAN LHC Events · Around

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