Optimizing observables with machine learning for better unfolding

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 level. We point out that while particle-level needs be physically motivated link with theory, detector-level need not can optimized. show using deep learning define observables has capability improve measurement when combined standard methods.