论文标题
Superraenn：在Pan-Starrs1 Medium Deep Survey Supernovae中训练的半监督超新星光度分类管道
SuperRAENN: A Semi-supervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium Deep Survey Supernovae
论文作者
论文摘要
基于光学光度光曲线信息的超新星（SNE）的自动分类在即将到来的广阔时间域调查的时代至关重要，例如鲁宾天文台进行的时空和时间（LSST）的传统调查。光度分类可以实时鉴定有趣的事件，以进行扩展的多波长随访以及档案人群研究。在这里，我们介绍了Pan-Starrs1中学调查（PS1-MDS）的5,243个“类似SN样”光曲线（in Griz）的完整样本。就节奏，过滤器和深度而言，PS1-MDS与计划的LSST广泛深入调查相似，使其成为社区的有用培训。使用此数据集，我们训练一种新型的半监督机学习算法，以光学法规对2,315种新的SN样光曲线和宿主星系光谱红移。我们的算法包括一个随机的森林监督分类步骤和一个新颖的无监督步骤，在该步骤中，我们引入了一个经常性的自动编码器神经网络（RAENN）。我们的最终管道（称为Superraenn）在五个SN类（IA，IBC，II，IIN，IIN，SLSN-I）的精度为87％。我们发现IA SNE型和SLSNE型的准确率最高，而IBC SNE型的最低率是最低的。我们从光谱镜和光度分类的样品分解为：62.0％IA型（1839个对象），19.8％II型（553个对象），4.8％类型IIN（136个对象），11.7％IBC型IBC（291个对象）和1.6％I型I SLSNE SLSNE（54个对象）。最后，我们讨论如何为在线LSST数据流修改此算法。
Automated classification of supernovae (SNe) based on optical photometric light curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin Observatory. Photometric classification can enable real-time identification of interesting events for extended multi-wavelength follow-up, as well as archival population studies. Here we present the complete sample of 5,243 "SN-like" light curves (in griz) from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS). The PS1-MDS is similar to the planned LSST Wide-Fast-Deep survey in terms of cadence, filters and depth, making this a useful training set for the community. Using this dataset, we train a novel semi-supervised machine learning algorithm to photometrically classify 2,315 new SN-like light curves with host galaxy spectroscopic redshifts. Our algorithm consists of a random forest supervised classification step and a novel unsupervised step in which we introduce a recurrent autoencoder neural network (RAENN). Our final pipeline, dubbed SuperRAENN, has an accuracy of 87% across five SN classes (Type Ia, Ibc, II, IIn, SLSN-I). We find the highest accuracy rates for Type Ia SNe and SLSNe and the lowest for Type Ibc SNe. Our complete spectroscopically- and photometrically-classified samples break down into: 62.0% Type Ia (1839 objects), 19.8% Type II (553 objects), 4.8% Type IIn (136 objects), 11.7% Type Ibc (291 objects), and 1.6% Type I SLSNe (54 objects). Finally, we discuss how this algorithm can be modified for online LSST data streams.
