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 ∼144 million new classifications while expanding the the total number of sources in PS1 PSC by ∼10%. We find that the new methodology, which utilizes PS1 forced photometry, performs ∼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 ∼144 million new classifications to the PS1 PSC will improve the efficiency with which transients are discovered.

我们对 PanSTARRS-1 点源目录 (PS1 PSC) 进行了更新，该目录提供了 PS1 源的形态分类。最初的 PS1 PSC 采用了严格的检测标准，从 PSC 中排除了数亿个 PS1 源。在这里，我们调整了用于创建 PS1 PSC 的监督机器学习方法，并将它们应用于更广泛可用的不同光度测量，使我们能够添加 ~1.44 亿个新分类，同时将 PS1 PSC 中的源总数扩展 ~ 10%。我们发现使用 PS1 强制测光的新方法的性能比原始方法差 6%–8%。这种性能的轻微下降被目录大小的整体增加所抵消。PS1 PSC 被时域调查用于过滤瞬态警报流，方法是删除与可能起源于银河系的点源重合的候选者。向 PS1 PSC 添加约 1.44 亿个新分类将提高发现瞬变的效率。