  We present a fast simulation application based on a Deep Neural Network,
designed to create large analysis-specific datasets. Taking as an example the
generation of W+jet events produced in sqrt(s)= 13 TeV proton-proton
collisions, we train a neural network to model detector resolution effects as a
transfer function acting on an analysis-specific set of relevant features,
computed at generation level, i.e., in absence of detector effects. Based on
this model, we propose a novel fast-simulation workflow that starts from a
large amount of generator-level events to deliver large analysis-specific
samples. The adoption of this approach would result in about an
order-of-magnitude reduction in computing and storage requirements for the
collision simulation workflow. This strategy could help the high energy physics
community to face the computing challenges of the future High-Luminosity LHC.
