How to GAN LHC events
Anja Butter, Tilman Plehn, Ramon Winterhalder
SciPost Phys. 7, 075 (2019) · published 4 December 2019
- doi: 10.21468/SciPostPhys.7.6.075
- Submissions/Reports
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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. It can be extended in a straightforward manner to include for instance off-shell contributions, higher orders, or approximate detector effects.
TY - JOUR
PB - SciPost Foundation
DO - 10.21468/SciPostPhys.7.6.075
TI - How to GAN LHC events
PY - 2019/12/04
UR - https://scipost.org/SciPostPhys.7.6.075
JF - SciPost Physics
JA - SciPost Phys.
VL - 7
IS - 6
SP - 075
A1 - Butter, Anja
AU - Plehn, Tilman
AU - Winterhalder, Ramon
AB - 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.
ER -@Article{10.21468/SciPostPhys.7.6.075,
title={{How to GAN LHC events}},
author={Anja Butter and Tilman Plehn and Ramon Winterhalder},
journal={SciPost Phys.},
volume={7},
pages={075},
year={2019},
publisher={SciPost},
doi={10.21468/SciPostPhys.7.6.075},
url={https://scipost.org/10.21468/SciPostPhys.7.6.075},
}
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