Dortmund 2021 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 38: Data analysis, information technology II

T 38.8: Vortrag

Dienstag, 16. März 2021, 17:45–18:00, Tm

GANplifying Event SamplesAnja Butter1, •Sascha Diefenbacher2, Gregor Kasieczka2, Benjamin Nachman3, and Tilman Plehn11Institut für Theoretische Physik, Universität Heidelberg, Deutschland — 2Institut für Experimentalphysik, Universität Hamburg, Deutschland — 3Physics Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Generative machine learning models have been successfully used in order to speed up or augment many simulation tasks in particle physics, ranging from event generation to fast calorimeter simulation to many more. This indicates that generative models have great potential to become a mainstay in many simulation chains. One question that still needs to be addressed, however, is whether the data produced by a generative model can offer increased precision compared to the data the model was originally trained on. In other words, can one meaningfully draw more samples from a generative model than the ones it was trained with. We explore this using a simplified model and demonstrate that generative models indeed have the capability to amplify data sets.

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