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 / A. Butter, T. Plehn, R. Winterhalder. - In: SCIPOST PHYSICS. - ISSN 2542-4653. - 7:6(2019 Apr 12), pp. 075.1-075.16. [10.21468/SciPostPhys.7.6.075]

How to GAN LHC events

R. Winterhalder
Ultimo
2019

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. It can be extended in a straightforward manner to include for instance off-shell contributions, higher orders, or approximate detector effects.
Settore PHYS-02/A - Fisica teorica delle interazioni fondamentali, modelli, metodi matematici e applicazioni
12-apr-2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1187520
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