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.

翻译：太阳光谱数据因其复杂的结构和丰富的细节，加之近期数据量的激增，带来了显著的处理挑战。为解决此问题，我们提出了一种基于深度学习的压缩技术，该技术利用Hinode SOT/SP数据开发的深度自编码器（DAE）和一维卷积自编码器（CAE）模型。我们专注于压缩来自宁静太阳和活动区的斯托克斯I和V偏振光谱，并通过纳入来自极端磁场的谱线，为全面的光谱分析提供了新颖的视角。结果表明，在重建斯托克斯轮廓方面，CAE模型优于DAE模型，表现出更强的鲁棒性，并将重建误差降至观测噪声水平附近。所提出的方法在压缩来自宁静太阳和活动区的斯托克斯I和V光谱方面已被证明是有效的，突显了其在太阳光谱分析中（例如异常光谱信号检测）具有重要应用潜力。