  Histogram-based template fits are the main technique used for estimating
parameters of high energy physics Monte Carlo generators. Parametrized neural
network reweighting can be used to extend this fitting procedure to many
dimensions and does not require binning. If the fit is to be performed using
reconstructed data, then expensive detector simulations must be used for
training the neural networks. We introduce a new two-level fitting approach
that only requires one dataset with detector simulation and then a set of
additional generation-level datasets without detector effects included. This
Simulation-level fit based on Reweighting Generator-level events with Neural
networks (SRGN) is demonstrated using simulated datasets for a variety of
examples including a simple Gaussian random variable, parton shower tuning, and
the top quark mass extraction.
