  There have been many applications of deep neural networks to detector
calibrations and a growing number of studies that propose deep generative
models as automated fast detector simulators. We show that these two tasks can
be unified by using maximum likelihood estimation (MLE) from conditional
generative models for energy regression. Unlike direct regression techniques,
the MLE approach is prior-independent and non-Gaussian resolutions can be
determined from the shape of the likelihood near the maximum. Using an
ATLAS-like calorimeter simulation, we demonstrate this concept in the context
of calorimeter energy calibration.
