Machine-learning selection of optical transients in the Subaru/Hyper Suprime-Cam survey

We present an application of machine-learning (ML) techniques to source selection in the optical transient survey data with the Hyper Suprime-Cam (HSC) on the Subaru telescope. Our goal is to select real transient events accurately and in a timely manner out of a large number of false candidates, obtained by the standard difference-imaging method. We have developed the transient selector, which is based on majority voting of the three ML machines of AUC Boosting, Random Forest, and Deep Neural Networks. We applied it to our observing runs of Subaru-HSC in 2015 May and August, and proved it to be efficient in selecting optical transients. The false positive rate was 1.0% at the true positive rate of 90% in the magnitude range of 22.0–25.0 mag for the May data. For the August run, we successfully detected and reported 10 supernovae candidates within the same day as the observation. From these runs, we learned the following lessons: (1) training using artificial objects is effective in filtering false candidates out, especially for faint objects, and (2) a combination of ML by majority voting is advantageous.