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：深度神经网络在探测器校准中有诸多应用，且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，通过使用来自条件生成模型的最大似然估计（MLE）进行能量回归，这两个任务可以统一起来。与直接回归技术不同，MLE方法不依赖先验信息，且非高斯分辨力可通过最大值附近似然函数的形状确定。通过类ATLAS量能器模拟，我们在量能器能量校准场景中验证了这一概念。