Photometric classification of Hyper Suprime-Cam transients using machine learning

Ichiro TAKAHASHI, Nao SUZUKI, Naoki YASUDA, Akisato KIMURA, Naonori UEDA, Masaomi TANAKA, Nozomu TOMINAGA, Naoki YOSHIDA

研究成果: Article査読

1 被引用数 (Scopus)

抄録

The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries. The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through spring 2017, yielded 1824 SN candidates. This gave rise to the need for fast type classification for spectroscopic follow-up and prompted us to develop amachine learning algorithm using a deep neural network with highway layers. This algorithm is trained by actual observed cadence and filter combinations such that we can directly input the observed data array without any interpretation. We tested our model with a dataset from the LSST classification challenge (Deep Drilling Field). Our classifier scores an area under the curve (AUC) of 0.996 for binary classification (SN Ia or non-SN Ia) and 95.3% accuracy for three-class classification (SN Ia, SN Ibc, or SN II). Application of our binary classification to HSC transient data yields an AUC score of 0.925. With two weeks of HSC data since the first detection, this classifier achieves 78.1% accuracy for binary classification, and the accuracy increases to 84.2% with the full dataset. This paper discusses the potential use of machine learning for SN type classification purposes.

本文言語English
論文番号psaa082
ジャーナルPublications of the Astronomical Society of Japan
72
5
DOI
出版ステータスPublished - 2020 10 1

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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