Abstract:
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.