Joint machine learning and analytic track reconstruction for X-ray polarimetry with gas pixel detectors

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 ($\sim 1\%$ at 3 keV and $\sim 6\%$ at 6 keV) and naturally mitigates a subtle effect appearing in polarization measurements of bright extended sources known as "polarization leakage".

References in corpus (11)

• An efficient photoelectric X-ray Polarimeter for the study of Black Holes and Neutron Stars
• The enhanced X-ray Timing and Polarimetry mission - eXTP
• Analyzing the Data from X-ray Polarimeters with Stokes Parameters
• Design, Construction, and Test of the Gas Pixel Detectors for the IXPE Mission
• A weighted analysis to improve the X-ray polarization sensitivity of IXPE
• Gas Pixel Detectors for X-ray Polarimetry applications
• HexagDLy - Processing hexagonally sampled data with CNNs in PyTorch
• Deep Ensemble Analysis for Imaging X-ray Polarimetry
• A convolutional neural network approach for reconstructing polarization information of photoelectric X-ray polarimeters
• Towards Optimal Signal Extraction for Imaging X-ray Polarimetry
• Polarisation Leakage due to Errors in Track Reconstruction in Gas Pixel Detectors

Cited by in corpus (1)

• Mitigating Polarization Leakage in Gas Pixel Detectors through Hybrid Machine Learning and Analytic Event Reconstruction