Coakley Kevin J, Splett Jolene, Gerrits Thomas
National Institute of Standards and Technology, Boulder CO 80305, USA.
National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
J Opt Soc Am B. 2022 Jan 1;39(1). doi: 10.1364/JOSAB.440232.
To calibrate an optical transition edge sensor (TES), for each pulse of the light source (e.g. pulsed laser), one must determine the ratio of the expected number of photons that deposit energy and the expected number of photons created by the laser. Based on the estimated pulse height generated by each energy deposit, we form a pulse height spectrum with features corresponding to different numbers of deposited photons. We model the number of photons that deposit energy per laser pulse as a realization of a Poisson process, and the observed pulse height spectrum with a mixture model method. For each candidate feature set, we determine the expected number of photons that deposit energy per pulse and its associated uncertainty based on the mixture model weights corresponding to that candidate feature set. From training data, we select the optimal feature set according to an uncertainty minimization criterion. We then determine the expected number of photons that deposit energy per pulse and its associated uncertainty for test data that is independent of the training data. Our uncertainty budget accounts for random measurement errors, systematic effects due to mismodeling feature shapes in our mixture model, and possible imperfections in our feature set selection method.
为了校准光学跃迁边缘传感器（TES），对于光源的每个脉冲（例如脉冲激光器），必须确定沉积能量的预期光子数与激光器产生的预期光子数之比。基于每个能量沉积产生的估计脉冲高度，我们形成一个脉冲高度谱，其特征对应于不同数量的沉积光子。我们将每个激光脉冲中沉积能量的光子数建模为泊松过程的一个实现，并使用混合模型方法对观测到的脉冲高度谱进行建模。对于每个候选特征集，我们根据与该候选特征集对应的混合模型权重，确定每个脉冲中沉积能量的预期光子数及其相关不确定性。从训练数据中，我们根据不确定性最小化标准选择最优特征集。然后，我们为独立于训练数据的测试数据确定每个脉冲中沉积能量的预期光子数及其相关不确定性。我们的不确定性预算考虑了随机测量误差、由于我们混合模型中特征形状建模错误导致的系统效应，以及我们特征集选择方法中可能存在的缺陷。
