We present a method for enhancing the sensitivity of X-ray telescopic observations with imaging polarimeters, with a focus on the gas pixel detectors (GPDs) to be flown on the Imaging X-ray Polarimetry Explorer (IXPE). Our analysis determines photoelectron directions, X-ray absorption points and X-ray energies for 1-9 keV event tracks, with estimates for both the statistical and model (reconstruction) uncertainties. We use a weighted maximum likelihood combination of predictions from a deep ensemble of ResNet convolutional neural networks, trained on Monte Carlo event simulations. We define a figure of merit to compare the polarization bias–variance trade-off in track reconstruction algorithms. For power-law source spectra, our method improves on the current planned IXPE analysis (and previous deep learning approaches), providing $\sim 45$% increase in effective exposure times. For individual energies, our method produces 20%–30% absolute improvements in modulation factor for simulated 100% polarized events, while keeping residual systematic modulation within $1\sigma$ of the finite sample minimum. Absorption point location and photon energy estimates are also significantly improved. We have validated our method with sample data from real GPD detectors.

我们提出了一种通过成像旋光仪提高X射线望远镜观测灵敏度的方法，重点是要在成像X射线偏振仪资源管理器（IXPE）上飞行的气体像素探测器（GPD）。我们的分析确定了1-9 keV事件轨迹的光电子方向，X射线吸收点和X射线能量，并估计了统计和模型（重建）不确定性。我们使用ResNet卷积神经网络的深层集成（经过蒙特卡洛事件模拟训练）的预测的加权最大似然组合。我们定义了一个品质因数，以比较轨道重建算法中的极化偏差-方差折衷。对于幂律源谱，我们的方法改进了当前计划的IXPE分析（和以前的深度学习方法），提供了$〜45$有效曝光时间增加％。对于单个能量，我们的方法可对模拟的100％极化事件的调制因子产生20％–30％的绝对改善，同时将残留的系统调制保持在$\mathrm{1个}\sigma$有限样本最小值。吸收点的位置和光子能量的估计也大大改善。我们已经使用来自实际GPD检测器的样本数据验证了我们的方法。