Exploring Damping Properties of IRIS Bright Points using Deep Learning Techniques

TL;DR

This paper investigates the damping properties of solar bright points using deep learning to analyze Doppler shift data from IRIS, revealing regional variations in damping rates and insights into energy dissipation in the solar atmosphere.

Contribution

It introduces a deep learning approach to analyze damping in solar bright points, providing new insights into their behavior across different solar regions.

Findings

Higher damping rates in coronal hole areas

Identification of periodic perturbations in Doppler velocities

Enhanced understanding of energy dissipation in the solar atmosphere

Abstract

This study analyzed the Doppler shift in the solar spectrum using the Interface Region Imaging Spectrograph (IRIS). Two types of oscillations were investigated: long period damp and short period damp. The researchers observed periodic perturbations in the Doppler velocity oscillations of bright points (BPs) in the chromosphere and transition region (TR). Deep learning techniques were used to examine the statistical properties of damping in different solar regions. The results showed variations in damping rates, with higher damping in coronal hole areas. The study provided insights into the damping behavior of BPs and contributed to our understanding of energy dissipation processes in the solar chromosphere and TR.