Feature-Based Classification Neural Network for Kepler Light Curves from Quarter 1

Abstract

Determining the types of light curves has been a challenge due to the massive amount of light curves generated by large sky survey programs. In the literature, the light curves classification methods are overly dependent on the imaging quality of the light curves, so the classification results are often poor. In this paper, a new method is proposed to classify the Kepler light curves from Quarter 1, and consists of two parts: feature extraction and classification neural network construction. In the first part, features are extracted from the light curves using three different methods, and then the features are fused (transform domain features, light curve flux statistics features, and Kepler photometry features). In the second part, a classification neural network RLNet, based on Residual Network (ResNet) and Long Short Term Memory (LSTM), is proposed. The experiment involved the classification of approximately 150,000 Kepler light curves into 11 categories. The results show that this new method outperforms seven other methods in all metrics, with an accuracy of 0.987, a minimum recall of 0.968, and a minimum precision of 0.970 under all categories.