X-ray polarimetry will soon open a new window on the high energy universe with the launch of NASA's Imaging X-ray Polarimetry Explorer (IXPE). Polarimeters are currently limited by their track reconstruction algorithms, which typically use linear estimators and do not consider individual event quality. We present a modern deep learning method for maximizing the sensitivity of X-ray telescopic observations with imaging polarimeters, with a focus on the gas pixel detectors (GPDs) to be flown on IXPE. We use a weighted maximum likelihood combination of predictions from a deep ensemble of ResNets, trained on Monte Carlo event simulations. We derive and apply the optimal event weighting for maximizing the polarization signal-to-noise ratio (SNR) in track reconstruction algorithms. For typical power-law source spectra, our method improves on the current state of the art, providing a ~40% decrease in required exposure times for a given SNR.

翻译：X射线极地测量不久将随着美国航天局的X射线极地测量仪(IXPE)的发射,在高能宇宙上打开一个新的窗口。目前,极光仪受到其轨道重建算法的限制,这些算法通常使用线性测算器,而不考虑个别事件的质量。我们提出了一个现代深层次的学习方法,用成像极光来最大限度地提高X射线远程观测的灵敏度,重点是将发射在IXPE上的气体像素探测器(GPDs)。我们使用一种加权的最大可能性组合,结合了在蒙特卡洛事件模拟中受过训练的深层ResNet的预测。我们在轨重建算法中使用最佳事件加权权重,以尽量扩大极分线信号比。对于典型的电法源光谱,我们的方法改进了当前的技术状态,为特定的SNR提供了所需的接触时间的~40%。