  A common setting for scientific inference is the ability to sample from a
high-fidelity forward model (simulation) without having an explicit probability
density of the data. We propose a simulation-based maximum likelihood
deconvolution approach in this setting called OmniFold. Deep learning enables
this approach to be naturally unbinned and (variable-, and) high-dimensional.
In contrast to model parameter estimation, the goal of deconvolution is to
remove detector distortions in order to enable a variety of down-stream
inference tasks. Our approach is the deep learning generalization of the common
Richardson-Lucy approach that is also called Iterative Bayesian Unfolding in
particle physics. We show how OmniFold can not only remove detector
distortions, but it can also account for noise processes and acceptance
effects.
