Inferring astrophysical X-ray polarization with deep learning
This work addresses polarization detection for astrophysical X-ray sources, which is incremental as it applies deep learning to a specific domain problem.
The paper tackled the problem of estimating impact points and polarization directions from X-ray data for the IXPE mission using deep learning, showing that data-driven approaches are a promising alternative to existing analytical methods.
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.