Dortmund 2021 – scientific programme

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T: Fachverband Teilchenphysik

T 38: Data analysis, information technology II

T 38.8: Talk

Tuesday, March 16, 2021, 17:45–18:00, Tm

GANplifying Event Samples — Anja Butter¹, •Sascha Diefenbacher², Gregor Kasieczka², Benjamin Nachman³, and Tilman Plehn¹ — ¹Institut 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.