  Machine learning techniques are widely applied in many modern optical sky
surveys, e.q. Pan-STARRS1, PTF/iPTF and Subaru/Hyper Suprime-Cam survey, to
reduce human intervention for data verification. In this study, we have
established a machine learning based real-bogus system to reject the false
detections in the Subaru/Hyper-Suprime-Cam StrategicSurvey Program (HSC-SSP)
source catalog. Therefore the HSC-SSP moving object detection pipeline can
operate more effectively due to the reduction of false positives. To train the
real-bogus system, we use the stationary sources as the real training set and
the "flagged" data as the bogus set. The training set contains 47 features,
most of which are photometric measurements and shape moments generated from the
HSC image reduction pipeline (hscPipe). Our system can reach a true positive
rate (tpr) ~96% with a false positive rate (fpr) ~ 1% or tpr ~99% at fpr ~5%.
Therefore we conclude that the stationary sources are decent real training
samples, and using photometry measurements and shape moments can reject the
false positives effectively.
