Deep-Learning-Based Identification of Broad-Absorption Line Quasars

The accurate classification of broad-absorption line (BAL) quasars and non-broad-absorption line (non-BAL) quasars is key in understanding active galactic nuclei (AGN) and the evolution of the universe. With the rapid accumulation of data from large-scale spectroscopic survey projects (e.g., LAMOST, SDSS, and DESI), traditional manual classification methods face limitations. In this study, we propose a new method based on deep learning techniques to achieve an accurate distinction between BAL quasars and non-BAL quasars. We use a convolutional neural network (CNN) as the core model, in combination with various dimensionality reduction techniques, including principal component analysis (PCA), t-distributed stochastic neighborhood embedding (t-SNE), and isometric mapping (ISOMAP). These dimensionality reduction methods help extract meaningful features from high-dimensional spectral data while reducing model complexity. We employ quasar spectra from the 16th data release (DR16) of the Sloan Digital Sky Survey (SDSS) and obtain classification labels from the DR16Q quasar catalogues to train and evaluate our model. Through extensive experiments and comparisons, the combination of PCA and CNN achieve a test accuracy of 99.11%, demonstrating the effectiveness of deep learning for classifying the spectral data. Additionally, we explore other dimensionality reduction methods and machine learning models, providing valuable insights for future research in this field.