Compression Method for Solar Polarization Spectra Collected from Hinode SOT/SP Observations
This addresses data processing bottlenecks for solar physicists analyzing Hinode telescope observations, though it appears incremental as it applies existing deep learning architectures to this specific domain.
The authors tackled the challenge of compressing large volumes of solar polarization spectra data by proposing deep autoencoder and 1D-convolutional autoencoder models, finding that the convolutional model outperformed with reconstruction errors near observational noise levels.
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.