论文标题
X射线偏振计数据的神经网络分析
Neural network analysis of X-ray polarimeter data
论文作者
论文摘要
本章介绍了基于神经网络的深度方法，以增强使用成像极光计的X射线望远镜观察的灵敏度。深度神经网络可用于确定来自2D光电子轨道图像的光电发射方向，光子吸收点以及光子能量，并估计统计和模型不确定性。深度神经网络预测不确定性可以纳入加权最大似然，以估计源极化参数。在基准气体体积之外转换的事件，其轨道几乎没有极化敏感性，这使极化估计复杂化。深度神经网络的分类器可用于选择反对这些事件，以提高能量分辨率和极化敏感性。将深神经网络方法的性能与标准数据分析方法进行了比较，这表明IXPE特异性模拟的最小可检测到极化的提高<0.75倍。讨论了这些方法的潜在发展和改进。
This chapter presents deep neural network based methods for enhancing the sensitivity of X-ray telescopic observations with imaging polarimeters. Deep neural networks can be used to determine photoelectron emission directions, photon absorptions points, and photon energies from 2D photoelectron track images, with estimates for both the statistical and model uncertainties. Deep neural network predictive uncertainties can be incorporated into a weighted maximum likelihood to estimate source polarization parameters. Events converting outside of the fiducial gas volume, whose tracks have little polarization sensitivity, complicate polarization estimation. Deep neural network based classifiers can be used to select against these events to improve energy resolution and polarization sensitivity. The performance of deep neural network methods is compared against standard data analysis methods, revealing a < 0.75x improvement in minimum detectable polarization for IXPE-specific simulations. Potential future developments and improvements to these methods are discussed.
