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.

中文翻译：

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使用机器学习对 Hyper Suprime-Cam 瞬变进行光度分类

技术的进步导致超新星 (SN) 发现的迅速增加。从 2016 年秋季到 2017 年春季进行的 Subaru/Hyper Suprime-Cam (HSC) 瞬态调查产生了 1824 名 SN 候选者。这引起了对光谱后续快速类型分类的需求，并促使我们使用具有高速公路层的深度神经网络 (DNN) 开发机器学习算法。这台机器是通过实际观察到的节奏和过滤器组合来训练的，这样我们就可以直接将观察到的数据数组输入到机器中而无需任何解释。我们使用来自 LSST 分类挑战（深钻油田）的数据集测试了我们的模型。我们的分类器对二元分类（SN Ia 或非 SN Ia）的曲线下面积 (AUC) 得分为 0.996，对三类分类（SN Ia，SN Ibc 或 SN II）。将我们的二元分类应用于 HSC 瞬态数据产生 0.925 的 AUC 分数。从第一次检测到两周的 HSC 数据，该分类器实现了 78.1% 的二元分类准确率，在完整数据集下准确率提高到 84.2%。本文讨论了机器学习在 SN 类型分类方面的潜在用途。