Kavli Affiliate: Nicola Omodei
| First 5 Authors: Nicoló Cibrario, Michela Negro, Raffaella Bonino, Nikita Moriakov, Luca Baldini
| Summary:
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 of the hybrid method
for both cases. Additionally, we demonstrated the algorithm’s effectiveness in
reducing the polarization leakage effect through the analysis of the IXPE
observation of the supernova remnant G21.5-0.9. By enabling more reliable
polarization measurements, this method can potentially yield deeper insights
into the magnetic field structures, particle acceleration processes, and
emission mechanisms at work within extended X-ray sources.
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