Speaker:
Anja Butter
Institution:
Heidelberg University, Germany
Date:
Wednesday, January 22, 2020
Time:
11:00 am
Location:
NS2 1201
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.
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.
Host:
Julian Heeck