FLEET: A Redshift-agnostic Machine Learning Pipeline to Rapidly Identify Hydrogen-poor Superluminous Supernovae
FLEET: A Redshift-agnostic Machine Learning Pipeline to Rapidly Identify Hydrogen-poor Superluminous Supernovae
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DOI：
10.3847/1538-4357/abbf49
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
S. Gomez;E. Berger;P. Blanchard;G. Hosseinzadeh;M. Nicholl;V. Villar;Yao Yin
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文献类型：
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作者：
S. Gomez;E. Berger;P. Blanchard;G. Hosseinzadeh;M. Nicholl;V. Villar;Yao Yin
在过去的十年中，宽视场光学时域调查已将瞬变的发现率提高到了 ≲10% 进行光谱分类的程度。尽管如此，这些调查仍然能够发现新的罕见瞬变类型，其中最著名的是贫氢超发光超新星 (SLSN-I) 类，迄今为止已确认了约 150 个事件。在这里，我们提出了一种机器学习分类算法，旨在快速识别 SLSN-I 纯样本，以实现光谱和多波长跟踪。该算法是寻找发光和奇异河外瞬变 (FLEET) 观测策略的一部分。它利用光曲线和上下文信息，但不需要红移，为每个新发现的瞬变分配一个 SLSN-I 的概率。当观察 SLSN-I 候选者的选择时，该分类器可以实现约 85% 的最大纯度（完整性为 20%）。此外，我们还提出了两种使用红移或完整光变曲线的替代分类器，可以实现更高的纯度和完整性。按照目前的发现速度，FLEET算法每年可以提供约20个SLSN-I候选物用于光谱随访，纯度为85%；通过遗产时空调查，我们预计每年的事件数量将超过事件数量。
Over the past decade wide-field optical time-domain surveys have increased the discovery rate of transients to the point that ≲10% are being spectroscopically classified. Despite this, these surveys have enabled the discovery of new and rare types of transients, most notably the class of hydrogen-poor superluminous supernovae (SLSN-I), with about 150 events confirmed to date. Here we present a machine-learning classification algorithm targeted at rapid identification of a pure sample of SLSN-I to enable spectroscopic and multiwavelength follow-up. This algorithm is part of the Finding Luminous and Exotic Extragalactic Transients (FLEET) observational strategy. It utilizes both light-curve and contextual information, but without the need for a redshift, to assign each newly discovered transient a probability of being a SLSN-I. This classifier can achieve a maximum purity of about 85% (with 20% completeness) when observing a selection of SLSN-I candidates. Additionally, we present two alternative classifiers that use either redshifts or complete light curves and can achieve an even higher purity and completeness. At the current discovery rate, the FLEET algorithm can provide about 20 SLSN-I candidates per year for spectroscopic follow-up with 85% purity; with the Legacy Survey of Space and Time we anticipate this will rise to more than events per year.