arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.03828astro-ph.IMastro-ph.HEcs.LGstat.ML
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用于成像X射线偏振测量的深包封分析

Deep Ensemble Analysis for Imaging X-ray Polarimetry

Harvard-Smithsonian Center for Astrophysics(哈佛-史密森尼天体物理中心);Universitá di Pisa(比萨大学);INFN-Sezione di Pisa(意大利国家核物理研究所比萨分部)

A. L. Peirson, R. W. Romani, H. L. Marshall, J. F. Steiner, L. Baldini

摘要:

提出了一种提高X射线望远镜成像偏振观测灵敏度的方法,重点研究了成像X射线偏振探测器(IXPE)上的气体像素探测器(GPD)。我们的分析确定光电子方向,X射线吸收点和X射线能量为1-9千电子伏的事件轨迹,统计和模型(重建)的不确定性的估计。我们使用来自ResNet卷积神经网络的深度集成的加权极大似然组合预测,这些预测是在蒙特卡洛事件模拟上训练的。我们定义了一个品质因数来比较在航迹重建算法中极化偏差-方差的权衡。对于幂律源光谱,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),有效曝光时间增加了约45%。对于单个能量,我们的方法产生20-30%的绝对改善调制因子模拟100%极化事件,同时保持残留系统调制在1西格玛的有限样本最小值。吸收点的位置和光子能量的估计也显着改善。我们已经验证了我们的方法与实际GPD探测器的样本数据。

英文摘要:

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 ~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.

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