Differential cross section measurements are the currency of scientific exchange in
particle and nuclear physics. The key challenge for these analyses is the correction
for detector distortions known as deconvolution or unfolding. In the case of binned
cross section measurements, there are many tools for regularized matrix inversion
where the matrix governs the detector response going from pre- to post-detector
observables. In this paper, we show how normalizing flows and neural posterior
estimation can be used for unfolding. This approach has many potential advantages,
including implicit regularization from the neural networks and fast inference from
amortized training. We demonstrate this approach using simple Gaussian examples
as well as a simulated jet substructure measurement at the Large Hadron Collider.
