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arXiv 1609.03249astro-ph.IM
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Subaru/Hyper Suprime-Cam测量中光学瞬变的机器学习选择

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

Mikio Morii, Shiro Ikeda, Nozomu Tominaga, Masaomi Tanaka, Tomoki Morokuma, Katsuhiko Ishiguro, Junji Yamato, Naonori Ueda, Naotaka Suzuki, Naoki Yasuda, Naoki Yoshida

备注：
9 pages, 6 figures. Accepted for publication in PASJ
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摘要：

我们提出了一个应用程序的机器学习（ML）技术选择源的光学瞬态调查数据与超Suprime-Cam（HSC）的斯巴鲁望远镜。我们的目标是准确地选择真正的瞬态事件，及时地从大量的虚假候选人，获得与标准的差异成像方法。我们已经开发了基于AUC Boosting，随机森林和深度神经网络三种ML机器的多数投票的瞬态选择器。我们将其应用于2015年5月和8月的Subaru-HSC观测运行，并证明了它在选择光学瞬态方面的有效性。在22.0--25.0等量级范围内，真阳性率为90%时，假阳性率为1.0%。对于后一次运行，我们在观测的同一天内成功地探测并报告了10颗超新星候选者。从这些运行中，我们学到了以下教训：（1）使用人工对象的训练在过滤错误候选项方面是有效的，特别是对于微弱的对象，以及（2）通过多数投票组合ML是有利的。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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