Mitigating Polarization Leakage in Gas Pixel Detectors through Hybrid Machine Learning and Analytic Event Reconstruction
Nicol\'o Cibrario, Michela Negro, Raffaella Bonino, Nikita Moriakov, Luca Baldini, Niccol\'o Di Lalla, Alessandro Di Marco, Sergio Fabiani, Andrea Frass\'a, Alessio Gorgi, Fabio La Monaca, Luca Latronico, Simone Maldera, Alberto Manfreda, Fabio Muleri, Nicola Omodei, John Rankin

TL;DR
This paper introduces a hybrid machine learning and analytic algorithm to improve the reconstruction of X-ray polarization data, significantly reducing polarization leakage in Gas Pixel Detectors for extended sources.
Contribution
The first application of a hybrid reconstruction method combining machine learning and analytic techniques to experimental IXPE data, effectively mitigating polarization leakage.
Findings
Hybrid method reliably reconstructs photoelectron tracks.
Significant reduction in polarization leakage in IXPE observations.
Enhanced accuracy in polarization measurements of extended X-ray sources.
Abstract
Spatially resolved polarization measurements of extended X-ray sources are expanding our understanding of the emission mechanisms and magnetic field properties involved. Such measurements have been possible in the past few years thanks to the Imaging X-ray Polarimetry Explorer (IXPE). However, the analysis of extended sources suffers a systematic effect known as polarization leakage, which artificially affects the measured polarization signal. To address this issue, we built a hybrid reconstruction algorithm, which combines machine learning and analytic techniques to improve the reconstruction of photoelectron tracks in the Gas Pixel Detector and to significantly mitigate polarization leakage. This work presents the first application of this hybrid method to experimental data, including both calibration lab measurements and IXPE observational data. We confirmed the reliable performance…
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