Photometrically-Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine Learning-Based Classification
Brian Hsu, Griffin Hosseinzadeh, V. Ashley Villar, Edo Berger
随着即将到来的 Vera C. Rubin 天文台空间和时间遗产巡天（LSST），预计所有瞬变源中只有约 0.1% 将获得光谱分类。为了研究稀有瞬变源，例如 I 型超亮超新星（SLSNe），我们必须转而依赖测光分类。沿此思路，本文对来自 Pan-STARRS1 中深度巡天（PS1-MDS）的 SLSNe 开展试点研究，这些天体使用我们的 SuperRAENN 和 Superphot 算法进行了测光分类。我们首先利用一组简单的选择指标从测光样本中构建子样本，这些指标旨在最小化污染并确保建模所需的充分数据质量。随后，我们使用瞬变源模块化开源拟合工具（Modular Open-Source Fitter for Transients，MOSFiT），以磁陀星自转减能模型拟合多波段光变曲线。通过将测光样本的磁陀星引擎参数和抛射物参数分布与 PS1-MDS 光谱样本以及更大规模的文献光谱样本进行比较，我们发现这些样本总体上是一致的，但测光样本延伸至更慢的自转速度和更低的抛射物质量，对应于更低光度的事件，这与测光选择的预期相符。尽管我们的 PS1-MDS 测光样本仍小于整体 SLSN 光谱样本，但我们的方法为 LSST 时代通过测光选择和研究使 SLSN 样本实现数量级增长铺平了道路。
英文摘要：
With the upcoming Vera C.~Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only $\sim 0.1\%$ of all transients will be classified spectroscopically. To conduct studies of rare transients, such as Type I superluminous supernovae (SLSNe), we must instead rely on photometric classification. In this vein, here we carry out a pilot study of SLSNe from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS) classified photometrically with our SuperRAENN and Superphot algorithms. We first construct a sub-sample of the photometric sample using a list of simple selection metrics designed to minimize contamination and ensure sufficient data quality for modeling. We then fit the multi-band light curves with a magnetar spin-down model using the Modular Open-Source Fitter for Transients (MOSFiT). Comparing the magnetar engine and ejecta parameter distributions of the photometric sample to those of the PS1-MDS spectroscopic sample and a larger literature spectroscopic sample, we find that these samples are overall consistent, but that the photometric sample extends to slower spins and lower ejecta masses, which correspond to lower luminosity events, as expected for photometric selection. While our PS1-MDS photometric sample is still smaller than the overall SLSN spectroscopic sample, our methodology paves the way to an orders-of-magnitude increase in the SLSN sample in the LSST era through photometric selection and study.
