YSO candidates from LAMOST LRS DR9 & ZTF

A wealth of light variation data have been accumulated, which provide rich samples for astronomers to study variable stars. Facing the challenge of big data, this article takes the classification of variable and transient objects observed by the Zwicky Transient Facility (ZTF) as the main goal, and a self-paced ensemble (SPE) imbalanced-learning classifier is constructed to separate different classes.

The performance of the classifier reveals that SPE is better than a traditional imbalanced-learning algorithm for the minority classes. In our work, the SPE classifier is satisfactory for young stellar objects (YSOs), and the completeness (recall) of YSOs is enhanced to 91%. As a result, 868,371 ZTF sources are classified into 15 classes by this classifier, which contains 8210 YSO candidates (YSO_prob>=0.70).