Compression Method for Solar Polarization Spectra Collected from Hinode SOT/SP Observations

Abstract

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.

References in corpus (11)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• The Interface Region Imaging Spectrograph (IRIS)
• The Solar Optical Telescope for the Hinode Mission: An Overview
• Dimensionality Reduction of SDSS Spectra with Variational Autoencoders
• Stokes Inversion based on Convolutional Neural Networks
• Machine learning initialization to accelerate Stokes profile inversions
• The intrinsic dimensionality of spectro-polarimetric data
• Autoencoding Galaxy Spectra I: Architecture
• Feature Extraction Techniques for the Analysis of Spectral Polarization Profiles
• The Minimum Description Length Principle and Model Selection in Spectropolarimetry