用深度学习压缩太阳偏振光谱，提升处理效率与精度。
Compression Method for Solar Polarization Spectra Collected from Hinode SOT/SP Observations
- 基于1D卷积自编码器压缩太阳偏振光谱数据
- 重建误差接近观测噪声水平，优于传统方法
- 适合太阳物理研究者用于高效分析磁活动区域光谱
太阳光谱数据结构复杂、细节丰富，且近年数据量激增，处理难度大。为此，我们提出一种基于深度学习的压缩方法，采用深自编码器（DAE）和一维卷积自编码器（CAE）模型，基于日食望远镜光谱仪（Hinode SOT/SP）数据构建。重点压缩宁静区及活动区的斯托克斯I和V偏振谱，首次将极端磁场条件下的光谱纳入综合分析。结果表明，CAE模型在重构斯托克斯谱方面表现更优，具有更强鲁棒性，重建误差接近观测噪声水平。该方法在压缩宁静区与活动区的斯托克斯I和V谱上均有效，展现出在太阳光谱分析中的重要应用潜力，如异常光谱信号检测。
原文摘要 · Abstract (English)
The complex structure and extensive details of solar spectral data, combined with a recent surge in volume, present significant processing challenges. To address this, we propose a deep learning-based compression technique using deep autoencoder (DAE) and 1D-convolutional autoencoder (CAE) models developed with Hinode SOT/SP data. We focused on compressing Stokes I and V polarization spectra from the quiet Sun, as well as from active regions, providing a novel insight into comprehensive spectral analysis by incorporating spectra from extreme magnetic fields. The results indicate that the CAE model outperforms the DAE model in reconstructing Stokes profiles, demonstrating greater robustness and achieving reconstruction errors around the observational noise level. The proposed method has proven effective in compressing Stokes I and V spectra from both the quiet Sun and active regions, highlighting its potential for impactful applications in solar spectral analysis, such as detection of unusual spectral signals.
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