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.

翻译：深度学习神经网络已广泛应用于探测器校准，同时越来越多研究提出将深度生成模型作为自动化快速探测器模拟器。我们证明，通过使用条件生成模型进行能量回归的最大似然估计（MLE），这两项任务可以统一实现。与直接回归技术不同，MLE方法具有先验独立性，且非高斯分辨率可由似然函数在最大值附近的形状确定。利用类ATLAS量能器模拟，我们在量能器能量校准的背景下验证了这一概念。