Automated analysis of oscillations in coronal bright points

TL;DR

This paper introduces a new wavelet-based method for automatically detecting and analyzing intensity oscillations in coronal bright points using solar imaging data, revealing characteristic periodicities and physical relationships.

Contribution

The study presents a novel, efficient wavelet-based technique for automatic detection and analysis of coronal bright points and their oscillations in solar images.

Findings

Detected significant periodicities at 4, 8-10, 17, 28, and 65 minutes.

Found bright point lifetimes follow a power law with exponent -1.13.

Established a power law relationship between BP lifetime and maximum diameter.

Abstract

Coronal bright points (BPs) are numerous, bright, small-scale dynamical features found in the solar corona. Bright points have been observed to exhibit intensity oscillations across a wide range of periodicities and are likely an important signature of plasma heating and/or transport mechanisms. We present a novel and efficient wavelet-based method that automatically detects and tracks the intensity evolution of BPs using images from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO) in the 193\r{A} bandpass. Through the study of a large, statistically significant set of BPs, we attempt to place constraints on the underlying physical mechanisms. We used a continuous wavelet transform (CWT) in 2D to detect the BPs within images. One-dimensional CWTs were used to analyse the individual BP time series to detect significant periodicities. We find significant periodicity at 4, 8-10, 17, 28, and 65 minutes. Bright point lifetimes are shown to follow a power law with exponent $-1.13\pm0.07$. The relationship between the BP lifetime and maximum diameter similarly follows a power law with exponent $0.129\pm0.011$. Our wavelet-based method successfully detects and extracts BPs and analyses their intensity oscillations. Future work will expand upon these methods, using larger datasets and simultaneous multi-instrument observations.