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<p>Track reconstruction algorithms are critical for polarization measurements. Convolutional neural networks (CNNs) are a promising alternative to traditional moment-based track reconstruction approaches. However, the hexagonal grid track images obtained using gas pixel detectors (GPDs) for better anisotropy do not match the classical rectangle-based CNN, and converting the track images from hexagonal to square results in a loss of information.</p><p>We developed a new hexagonal CNN algorithm for track reconstruction and polarization estimation in X-ray polarimeters, which was used to extract the emission angles and absorption points from photoelectron track images and predict the uncertainty of the predicted emission angles. The simulated data from the PolarLight test were used to train and test the hexagonal CNN models. For individual energies, the hexagonal CNN algorithm produced 15%–30% improvements in the modulation factor compared to the moment analysis method for 100% polarized data, and its performance was comparable to that of the rectangle-based CNN algorithm that was recently developed by the Imaging X-ray Polarimetry Explorer team, but at a lower computational and storage cost for preprocessing.</p>

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Research on the X-ray polarization deconstruction method based on hexagonal convolutional neural network

  • 期刊论文

  • 发表日期：

    2025年1月12日

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  • Ya-Nan Li,
  • Jia-Huan Zhu,
  • Huai-Zhong Gao,
  • Hong Li,
  • Ji-Rong Cang,
  • Zhi Zeng,
  • Hua Feng,
  • Ming Zeng

摘要

Track reconstruction algorithms are critical for polarization measurements. Convolutional neural networks (CNNs) are a promising alternative to traditional moment-based track reconstruction approaches. However, the hexagonal grid track images obtained using gas pixel detectors (GPDs) for better anisotropy do not match the classical rectangle-based CNN, and converting the track images from hexagonal to square results in a loss of information.

We developed a new hexagonal CNN algorithm for track reconstruction and polarization estimation in X-ray polarimeters, which was used to extract the emission angles and absorption points from photoelectron track images and predict the uncertainty of the predicted emission angles. The simulated data from the PolarLight test were used to train and test the hexagonal CNN models. For individual energies, the hexagonal CNN algorithm produced 15%–30% improvements in the modulation factor compared to the moment analysis method for 100% polarized data, and its performance was comparable to that of the rectangle-based CNN algorithm that was recently developed by the Imaging X-ray Polarimetry Explorer team, but at a lower computational and storage cost for preprocessing.