论文标题
识别未解决的PANSTARRS1来源的形态学分类模型II：更新到PS1点源目录
A Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources II: Update to the PS1 Point Source Catalog
论文作者
论文摘要
我们向Panstarrs-1点源目录（PS1 PSC）提供了更新，该目录提供了PS1源的形态分类。原始的PS1 PSC采用了严格的检测标准，该标准将数亿PS1源排除在PSC中。在这里，我们调整了用于创建PS1 PSC的监督机器学习方法，并将其应用于更广泛可用的不同光度测量值，从而使我们可以添加$ \ sim $ \ sim $ \ sim，同时将PS1 PSC中的源总数扩大到$ \ sim $ 10％。我们发现使用PS1强制光度法的新方法比原始方法差6-8％。这种略有性能的略有降解被目录的总体总体增加所抵消。时间域调查使用PS1 PSC来通过删除与可能在原点是银河系的点源相吻合的候选者来过滤瞬态警报流。在PS1 PSC中增加了$ \ sim $ 1.44亿美元的新分类将提高发现瞬变的效率。
We present an update to the PanSTARRS-1 Point Source Catalog (PS1 PSC), which provides morphological classifications of PS1 sources. The original PS1 PSC adopted stringent detection criteria that excluded hundreds of millions of PS1 sources from the PSC. Here, we adapt the supervised machine learning methods used to create the PS1 PSC and apply them to different photometric measurements that are more widely available, allowing us to add $\sim$144 million new classifications while expanding the the total number of sources in PS1 PSC by $\sim$10%. We find that the new methodology, which utilizes PS1 forced photometry, performs $\sim$6-8% worse than the original method. This slight degradation in performance is offset by the overall increase in the size of the catalog. The PS1 PSC is used by time-domain surveys to filter transient alert streams by removing candidates coincident with point sources that are likely to be Galactic in origin. The addition of $\sim$144 million new classifications to the PS1 PSC will improve the efficiency with which transients are discovered.
