太阳系通知警报处理系统（SNAPS）：小行星种群异常检测
The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
AI总结：
本文提出SNAPS系统，利用无监督方法及四种异常度量，在31,693个ZTF天体上计算异常分数，并探索32,752个特征空间，实现Rubin规模下每月种群异常检测。
摘要：
太阳系通知警报处理系统（SNAPS）是一个ZTF和Rubin天文台的警报代理，它将向社区发送有关太阳系中有趣事件的警报。SNAPS正在积极监测太阳系天体，其功能之一是将天体（主要是主带小行星）相互比较，以发现相对于种群而言的异常天体。在本文中，我们使用包含来自ZTF的31,693个天体的SNAPShot1数据集，并为每个天体计算异常分数。SNAPS采用无监督方法；因此，为了获得每个天体的异常排名，我们提出了四种不同的异常度量，以便探索异常分数的变体并为异常排名增加置信度。我们还为每个天体在15个特征空间的每种排列（特征数量从2到15）中提供异常分数，这产生了总共32,752个特征空间。我们展示了使用四块Nvidia A100 GPU可以在Rubin天文台规模下每月生成种群异常排名，并提出了若干可以利用种群异常检测进行科学研究的途径。
英文摘要：
The Solar System Notification Alert Processing System (SNAPS) is a ZTF and Rubin Observatory alert broker that will send alerts to the community regarding interesting events in the Solar System. SNAPS is actively monitoring Solar System objects and one of its functions is to compare objects (primarily main belt asteroids) to one another to find those that are outliers relative to the population. In this paper, we use the SNAPShot1 dataset which contains 31,693 objects from ZTF and derive outlier scores for each of these objects. SNAPS employs an unsupervised approach; consequently, to derive outlier rankings for each object, we propose four different outlier metrics such that we can explore variants of outlier scores and add confidence to outlier rankings. We also provide outlier scores for each object in each permutation of 15 feature spaces, between 2 and 15 features, which yields 32,752 total feature spaces. We show that we can derive population outlier rankings each month at Rubin Observatory scale using four Nvidia A100 GPUs, and present several avenues of scientific investigation that can be explored using population outlier detection.
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