Invertible Networks or Partons to Detector and Back Again

For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simulation in terms of high-level observables, specifically for ZW production at the LHC. It allows for a per-event statistical interpretation. Next, we allow for a variable number of QCD jets. We unfold detector effects and QCD radiation to a pre-defined hard process, again with a per-event probabilistic interpretation over parton-level phase space.

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25 pages, 10 figures

quantum chromodynamics: radiation
parton
hard scattering
neural network
CERN LHC Coll
gauge boson: production
jet
statistical

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