Speaker
Michela Paganini
(Yale University (US))
Description
In this contribution, we present a method for tuning perturbative parameters in Monte Carlo simulation using a classifier loss in high dimensions. We use an LSTM trained on the radiation pattern inside jets to learn the parameters of the final state shower in the Pythia Monte Carlo generator. This represents a step forward compared to unidimensional distributional template-matching methods.
| Intended contribution length | 20 minutes |
|---|
Authors
Luke Percival De Oliveira
Michela Paganini
(Yale University (US))
Ben Nachman
(University of California Berkeley (US))
Steve Mrenna
(Fermi National Accelerator Lab. (US))
Chase Owen Shimmin
(Yale University (US))
Paul Louis Tipton
(Physics Department - Yale University)
