Photometric Classification of 2315 Pan-STARRS1 Supernovae with Superphot
Photometric Classification of 2315 Pan-STARRS1 Supernovae with Superphot
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DOI：
10.3847/1538-4357/abc42b
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
G. Hosseinzadeh;F. Dauphin;V. Villar;E. Berger;David O. Jones;P. Challis;R. Chornock;M. Drout
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文献类型：
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作者：
G. Hosseinzadeh;F. Dauphin;V. Villar;E. Berger;David O. Jones;P. Challis;R. Chornock;M. Drout
超新星（SNe）的分类及其对我们理解爆炸物理和祖先的影响传统上是基于某些光谱特征的存在或不存在。然而，目前和即将到来的广域时域调查已经增加了瞬态发现率，远远超出了我们获得每个新事件的单个频谱的能力。因此，我们必须严重依赖于光度分类-连接SN光变曲线回到他们的光谱定义的类。在这里，我们介绍了Superphot，这是Villar等人的机器学习分类算法的开源Python实现，并将其应用于2315个以前未分类的瞬态从泛星1中深调查，我们获得了光谱主星系红移。我们的分类器实现了82%的总体准确度，最佳类别（SNe Ia和超发光SNe）的完整性和纯度>80%。对于表现最差的SN类（SNe Ibc），完整性和纯度分别下降到37%和21%。我们的分类器提供了1257个新分类的SNe Ia，521个SNe II，298个SNe Ibc，181个SNe IIn和58个SLSNe。这些都是最大的均匀观察到的样本SNe在文献中，并将使广泛的统计研究的每一类。
The classification of supernovae (SNe) and its impact on our understanding of explosion physics and progenitors have traditionally been based on the presence or absence of certain spectral features. However, current and upcoming wide-field time-domain surveys have increased the transient discovery rate far beyond our capacity to obtain even a single spectrum of each new event. We must therefore rely heavily on photometric classification—connecting SN light curves back to their spectroscopically defined classes. Here, we present Superphot, an open-source Python implementation of the machine-learning classification algorithm of Villar et al., and apply it to 2315 previously unclassified transients from the Pan-STARRS1 Medium Deep Survey for which we obtained spectroscopic host-galaxy redshifts. Our classifier achieves an overall accuracy of 82%, with completenesses and purities of >80% for the best classes (SNe Ia and superluminous SNe). For the worst performing SN class (SNe Ibc), the completeness and purity fall to 37% and 21%, respectively. Our classifier provides 1257 newly classified SNe Ia, 521 SNe II, 298 SNe Ibc, 181 SNe IIn, and 58 SLSNe. These are among the largest uniformly observed samples of SNe available in the literature and will enable a wide range of statistical studies of each class.