Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey

Abstract

We present an application of machine-learning (ML) techniques to source selection in the optical transient survey data with 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 with the standard difference-imaging method. We have developed the transient selector which is based on majority voting of three ML machines of AUC Boosting, Random Forest, and Deep Neural Network. 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 former data. For the latter run, we successfully detected and reported ten candidates of supernovae within the same day as the observation. From these runs, we learned the following lessons: (1) the training using artificial objects is effective in filtering out false candidates, especially for faint objects, and (2) combination of ML by majority voting is advantageous.

9 pages, 6 figures. Accepted for publication in PASJ

References in corpus (2)

• Machine learning for transient discovery in Pan-STARRS1 difference imaging
• How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging

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• Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys
• Data Compression for the Tomo-e Gozen with Low-rank Matrix Approximation
• Machine Learning for Observational Cosmology
• Single-epoch supernova classification with deep convolutional neural networks
• Automated Transient Detection with Shapelet Analysis in Image-subtracted Data