Speaker
Timo Janßen
(Georg-August-Universität Göttingen)
Description
Modern machine learning methods offer great potential for increasing the efficiency of Monte Carlo event generators. We present the latest developments in the context of the event generation framework SHERPA. These include phase space sampling using normalizing flows and a new unweighting procedure based on neural network surrogates for the full matrix elements. We discuss corresponding general construction criteria and show examples of efficiency gains for relevant LHC production processes.
| Speaker time zone | Compatible with Europe |
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Primary author
Timo Janßen
(Georg-August-Universität Göttingen)
