DPQP: A Detection Pipeline for Quasar Pair Candidates Based on QSO Photometric Images and Spectra

Abstract

Quasars have an important role in the studies of galaxy evolution and star formation. The rare close projection of two quasars in the sky allows us to study the environment and matter exchange around the foreground quasar (QSOfg) and the background quasar (QSObg). This paper proposes a pipeline DPQP for quasar pair (QP) candidates’ detection based on photometric images and the corresponding spectra. The pipeline consists of three main parts: a target source detector, a regressor, and a discriminator. In the first part, the target source detection network–YOLOv4 (TSD-YOLOv4) and the target source classification network (TSCNet) are used in sequence to detect quasars in SDSS photometric images. In the second part, a depth feature extraction network of quasar images (DE-QNet) is constructed to estimate the redshifts of quasars from photometric images. In the third part, a quasar pair score (Q-Score) metric is proposed based on the spectral analysis. The larger the Q-Score, the greater the possibility of two pairs being a quasar pair. The experimental results show that between redshift 1.0 and 4.0, the MAE of DE-QNet is 0.316, which is 16.1% lower than the existing method. Samples with |Δz| < 0.15 account for 77.1% of the test dataset. A new table with 1025 QP candidates is provided by traversing 50,000 SDSS photometric images.