论文标题
通过深度学习推断天体物理X射线极化
Inferring astrophysical X-ray polarization with deep learning
论文作者
论文摘要
我们研究了从天体物理来源检测X射线极化检测的深度学习的使用，这将通过成像X射线极化探索器（IXPE）观察到，这是一个未来的NASA选择的基于NASA的基于空间的任务，预计将在2021年进行操作。特别是，我们建议可以使用两个模型来估算影响点的影响点以及极化的散热界限。获得的结果表明，数据驱动的方法描述了现有分析方法的有希望的替代方法。我们还讨论了在不久的将来要解决的问题和挑战。
We investigate the use of deep learning in the context of X-ray polarization detection from astrophysical sources as will be observed by the Imaging X-ray Polarimetry Explorer (IXPE), a future NASA selected space-based mission expected to be operative in 2021. In particular, we propose two models that can be used to estimate the impact point as well as the polarization direction of the incoming radiation. The results obtained show that data-driven approaches depict a promising alternative to the existing analytical approaches. We also discuss problems and challenges to be addressed in the near future.
