TY - JOUR
T1 - Photometric classification of Hyper Suprime-Cam transients using machine learning
AU - TAKAHASHI, Ichiro
AU - SUZUKI, Nao
AU - YASUDA, Naoki
AU - KIMURA, Akisato
AU - UEDA, Naonori
AU - TANAKA, Masaomi
AU - TOMINAGA, Nozomu
AU - YOSHIDA, Naoki
N1 - Publisher Copyright:
© The Author(s) 2020.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - 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.
AB - 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.
KW - Methods: Statistical
KW - Supernovae: General
KW - Surveys
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U2 - 10.1093/pasj/psaa082
DO - 10.1093/pasj/psaa082
M3 - Article
AN - SCOPUS:85096639285
VL - 72
JO - Publications of the Astronomical Society of Japan
JF - Publications of the Astronomical Society of Japan
SN - 0004-6264
IS - 5
M1 - psaa082
ER -