BALNet: Deep-learning-based Detection and Measurement of Broad Absorption Lines in Quasar Spectra

Abstract Broad absorption line (BAL) quasars serve as critical probes for understanding outflows from active galactic nuclei, black hole accretion, and cosmic evolution. To address the limitations of manual classification in large-scale spectroscopic surveys—where the number of quasar spectra is growing exponentially—we propose BALNet , a deep learning approach consisting of a one-dimensional convolutional neural network and bidirectional long short-term memory networks to automatically detect BAL troughs in quasar spectra. BALNet enables both the identification of BAL quasars and the measurement of their BAL troughs. We construct a simulated dataset for training and testing by combining non-BAL quasar spectra and BAL troughs, both derived from Sloan Digital Sky Survey (SDSS) DR16 observations. Experimental results in the testing set show that: (1) BAL trough detection achieves 83.0% completeness, 90.7% purity, and an F1-score of 86.7%; (2) BAL quasar classification achieves 90.8% completeness and 94.4% purity; (3) the predicted BAL velocities agree closely with simulated ground-truth labels, confirming BALNet ’s robustness and accuracy. When applied to the SDSS DR16 data within the redshift range 1.5 < z < 5.7, at least one BAL trough is detected in 20.4% of spectra. Notably, more than a quarter of these are newly identified sources with significant absorption, 8.8% correspond to redshifted systems, and some narrow/weak absorption features were missed. BALNet greatly improves the efficiency of large-scale BAL trough detection and enables more effective scientific analysis of quasar spectra.