Title: Detection of kink oscillations in solar coronal loops by a CNN-LSTM neural network Show Chinese title

Title: 通过卷积神经网络-长短期记忆神经网络检测太阳日冕环的折曲振荡

Abstract: A hybrid machine learning model which combines a shallow convolutional neural network and a long short-term memory network (CNN--LSTM), 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 dataset. The model was then applied to detect kink oscillations in real data cubes of coronal active regions observed with SDO/AIA in the 171~\AA\ channel. This dataset consisted of 50 samples with visually detected kink oscillations and 128 samples without. Each sample covered an area of 260$\times$260~pixels in the spatial domain and a duration of 30~min with a 12~s cadence in the time domain. Both off-limb and on-disk regions of interest were used. The data were pre-processed by median filtering in the time domain, and Gaussian smoothing and Contrast Limited Adaptive Histogram Equalization in the spatial domain. In the real dataset, 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 study. Show Chinese abstract

Abstract: 一种结合浅层卷积神经网络和长短期记忆网络（CNN--LSTM）的混合机器学习模型已被开发出来，以自动化检测日冕等离子体环中弯曲振荡，在大量高时间分辨率的成像数据序列中进行检测。 该网络在一个由10,000个合成数据立方体组成的集上进行训练，这些数据立方体旨在模拟日冕图像序列，在这个合成数据集上的准确率超过了98%。 然后将该模型应用于检测用SDO/AIA在171~\AA 通道观测到的日冕活动区的真实数据立方体中的弯曲振荡。 该数据集包括50个具有视觉检测到的弯曲振荡的样本和128个没有弯曲振荡的样本。 每个样本在空间域覆盖260$\times$260像素的区域，并在时间域持续30分钟，时间分辨率为12秒。 使用了离边缘和盘面区域的兴趣区域。 数据在时间域通过中值滤波进行预处理，在空间域通过高斯平滑和对比度受限自适应直方图均衡化进行预处理。 在真实数据集中，模型的性能为83.7%。该模型完全以开放获取方式提供。 我们认为开发的CNN--LSTM模型是创建稳健工具以用于日冕振荡研究背景下常规日冕数据挖掘的第一步。