Detection of Kink Oscillations in Solar Coronal Loops by a Convolutional Neural Network–Long Short-term Memory Neural Network

This is the authors' abstract. We don't add key points for this paper.

A hybrid machine learning model, which combines a shallow convolutional neural network and a long short-term memory (CNN-LSTM) network, has been developed to automate the detection of kink oscillations in coronal plasma loops within large volumes of high-cadence sequences of imaging data. The network was trained on a set of 10,000 synthetic data cubes designed to mimic sequences of coronal images, achieving an accuracy greater than 98% on this synthetic data set. The model was then applied to detect kink oscillations in real data cubes of coronal active regions observed with Solar Dynamics Observatory/Atmospheric Imaging Assembly in the 171 Å channel. This data set consisted of 50 samples with visually detected kink oscillations and 128 samples without. Each sample covered an area of 260 × 260 pixels in the spatial domain and a duration of 30 minutes with a 12 s cadence in the time domain. Both off-limb and on-disk regions of interest were used. The data were preprocessed by median filtering in the time domain, and Gaussian smoothing and contrast-limited adaptive histogram equalization in the spatial domain. In the real data set, the performance of the model was 83.7%. The model is fully available in open access. We regard the CNN-LSTM model developed as a first step toward creating robust tools for routine solar coronal data mining in the context of coronal oscillation studies.