Event generation with normalizing flows

@article{Gao2020EventGW,
  title={Event generation with normalizing flows},
  author={Christina Gao and Stefan Hoeche and Joshua Isaacson and Claudius Krause and Holger Schulz},
  journal={Physical Review D},
  year={2020}
}
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… 

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