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

TL;DR

This paper introduces a deep learning-based compression method for solar polarization spectra from Hinode SOT/SP observations, effectively reducing data size while maintaining spectral integrity for analysis.

Contribution

It develops and compares deep autoencoder and convolutional autoencoder models for compressing solar spectral data, with the convolutional model showing superior performance.

Findings

CAE outperforms DAE in spectral reconstruction

Reconstruction errors are near observational noise levels

Effective compression of spectra from quiet Sun and active regions

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.