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Talk29 min

Adversarial Tuning of Perturbative Parameters in Non-Differentiable Physics Simulators

Michela Paganini · CERN

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About

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..

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The talk

  1. Adversarial Tuning of Perturbative Parameters in Non-Differentiable Physics Simulators 2018-04-10 · 29 min · CERN's player

Credits and licence

Institution
CERN
Department
Machine Learning
Speaker
Michela Paganini
Year
2018
Language
English
Subjects
Computer science
Source
CERN
Licence
CERN, as CERN states it. CERN's terms
Attribution
© CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
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