Home > Adversarial Tuning of Perturbative Parameters in Non-Differentiable Physics Simulators |
Talk | |||||||||||
Title | Adversarial Tuning of Perturbative Parameters in Non-Differentiable Physics Simulators | ||||||||||
Video |
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Author(s) | Paganini, Michela (speaker) (Yale University (US)) | ||||||||||
Corporate author(s) | CERN. Geneva | ||||||||||
Imprint | 2018-04-10. - Streaming video. | ||||||||||
Series | (Machine Learning) (2nd IML Machine Learning Workshop) | ||||||||||
Lecture note | on 2018-04-10T11:00:00 | ||||||||||
Subject category | Machine Learning | ||||||||||
Abstract | 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. | ||||||||||
Copyright/License | © 2018-2024 CERN | ||||||||||
Submitted by | paul.seyfert@cern.ch |