  Most measurements in particle and nuclear physics use matrix-based unfolding
algorithms to correct for detector effects. In nearly all cases, the observable
is defined analogously at the particle and detector level. We point out that
while the particle-level observable needs to be physically motivated to link
with theory, the detector-level need not be and can be optimized. We show that
using deep learning to define detector-level observables has the capability to
improve the measurement when combined with standard unfolding methods.
