Kavli Affiliate: Naoki Yasuda
| First 5 Authors: Ichiro Takahashi, Nao Suzuki, Naoki Yasuda, Akisato Kimura, Naonori Ueda
| Summary:
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 a machine learning algorithm using a deep neural
network (DNN) with highway layers. This machine is trained by actual observed
cadence and filter combinations such that we can directly input the observed
data array into the machine 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.
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