Abstract
We present our study on the reconstruction of photoelectron tracks in gas pixel detectors used for astrophysical X-ray polarimetry. Our work aims to maximize the performance of convolutional neural networks (CNNs) to predict the impact point of incoming X-rays from the image of the photoelectron track. A very high precision in the reconstruction of the impact point position is achieved thanks to the introduction of an artificial sharpening process of the images. We find that providing the CNN-predicted impact point as input to the state-of-the-art analytic analysis improves the modulation factor (~1% at 3 keV and ~6% at 6 keV) and naturally mitigates a subtle effect appearing in polarization measurements of bright extended sources known as “polarization leakage”.
Subject
Space and Planetary Science,Astronomy and Astrophysics
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. The Imaging x-ray polarimetry explorer (IXPE) at last!;UV, X-Ray, and Gamma-Ray Space Instrumentation for Astronomy XXIII;2023-10-05