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arXiv · 2001.10028

Event Generation with Normalizing Flows

Christina Gao·Stefan Hoeche·Joshua Isaacson·Claudius Krause·Holger Schulz
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Abstract

We present a novel integrator based on normalizing flows which can be used to improve the unweighting efficiency of Monte-Carlo event generators for collider physics simulations. In contrast to machine learning approaches based on surrogate models, our method generates the correct result even if the underlying neural networks are not optimally trained. We exemplify the new strategy using the example of Drell-Yan type processes at the LHC, both at leading and partially at next-to-leading order QCD.

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Christina Gao, Stefan Hoeche, Joshua Isaacson, Claudius Krause, Holger Schulz. 2020-01-27. Event Generation with Normalizing Flows. https://doi.org/10.1103/physrevd.101.076002

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