Dampening Long-Period Doppler Shift Oscillations using Deep Machine Learning Techniques in the Solar Network and Internetwork
Rayhaneh Sadeghi, Ehsan Tavabi

TL;DR
This paper uses deep learning to analyze Doppler shift oscillations in solar bright points, revealing damping behaviors that suggest wave energy transport in the solar atmosphere.
Contribution
It is the first study to investigate longitudinal damping oscillations in BPs using comprehensive Doppler velocity analysis with deep learning.
Findings
80% of network BPs in quiet areas show damping
Damping rates vary across regions, with active areas showing higher damping
All damping observed is underdamped, indicating wave propagation and energy leakage
Abstract
This study explores the Doppler shift at different wavelengths in the Interface Region Imaging Spectrograph (IRIS) solar spectrum and implements a comprehensive consideration of Doppler velocity oscillations in the IRIS channels. This comprehensive consideration reveals a propagating periodic perturbation in a large number of chromosphere and transition region (TR) bright points (BPs). To our knowledge, this is the first investigation of the longitudinal oscillations with damping in BPs using comprehensive consideration of the Doppler velocity at various wavelengths. The phenomena of attenuation in the red and blue Doppler shifts of the solar wavelength range were seen several times during the experiments. We utilized deep learning techniques to examine the statistical properties of damping in network and internetwork BPs, as well as active, quiet areas, and coronal hole areas. Our…
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Taxonomy
TopicsSpacecraft and Cryogenic Technologies
