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北美退伍军人节(Veterans Day),12 日无更新
arXiv 2111.03047astro-ph.IMcs.CVcs.LG
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X射线偏振测量的深系综方法

A deep ensemble approach to X-ray polarimetry

Kavli Institute for Particle Astrophysics and Cosmology(卡夫利粒子天体物理与宇宙学研究所);Stanford University(斯坦福大学)

A. L. Peirson, R. W. Romani

摘要:

随着美国宇航局成像X射线偏振探测器(IXPE)的发射,X射线偏振测量法将很快为高能宇宙打开一扇新的窗户。旋光仪目前受到其轨迹重建算法的限制,这些算法通常使用线性估计器并且不考虑单个事件质量。我们提出了一种现代深度学习方法,用于最大限度地提高使用成像偏振仪进行X射线望远镜观测的灵敏度,重点关注IXPE上的气体像素探测器(GPD)。我们使用了一个加权的极大似然组合预测从一个深合奏的ResNets,训练蒙特卡洛事件模拟。我们推导并应用最佳的事件加权最大化的极化信噪比(SNR)的轨道重建算法。对于典型的幂律源光谱,我们的方法改进了现有技术,对于给定的SNR,所需的曝光时间减少了约40%。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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