识别未分辨PanSTARRS1源的形态学分类模型：在ZTF实时流水线中的应用
A Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources: Application in the ZTF Real-Time Pipeline
摘要：
在大型光度测量时代，自动化和准确分类的重要性正在迅速增加。具体来说，在天文成像中分离已分辨和未分辨的源是从银河系科学到大尺度结构和宇宙学等广泛研究的关键初始步骤。在这里，我们提出了我们的方法来构建一个大的，深目录的点源利用泛星1（PS1）3 $π$调查数据，其中包括$\sim$ 3 $\times10^9$源与$m\lesssim23.5\,$杂志。我们开发了一个监督机器学习方法，使用随机森林（RF）算法，构建PS1形态模型。我们训练模型使用$\sim$ 5 $\times10^4$ PS1源与HST COSMOS形态分类，并评估其性能使用$\sim$ 4 $\times10^6$源与斯隆数字巡天（SDSS）光谱和$\sim$ 2 $\times10^8$ \textit{Gaia}源。我们构建了11个“白通量”特征，该特征将PS1通量和形状测量跨5个滤波器相结合，以相对于任何单个滤波器增加信噪比。RF模型进行了比较，3个替代模型，包括SDSS和PS1光度分类模型，我们发现，RF模型表现最好。从数量上看，PS1星表主要由微弱的源（$m\gtrsim21\,$等）组成，在这种情况下，RF模型的表现明显优于SDSS和PS1模型。对于时域调查，识别未解决的源对于推断新瞬变的银河系或河外起源至关重要。我们已经分类$\sim$ 1.5 $\times10^9$源使用RF模型，这些结果被用于Zwicky瞬态设施实时管道自动拒绝恒星源从河外警报流。
英文摘要：
In the era of large photometric surveys, the importance of automated and accurate classification is rapidly increasing. Specifically, the separation of resolved and unresolved sources in astronomical imaging is a critical initial step for a wide array of studies, ranging from Galactic science to large scale structure and cosmology. Here, we present our method to construct a large, deep catalog of point sources utilizing Pan-STARRS1 (PS1) 3$π$ survey data, which consists of $\sim$3$\times10^9$ sources with $m\lesssim23.5\,$mag. We develop a supervised machine-learning methodology, using the random forest (RF) algorithm, to construct the PS1 morphology model. We train the model using $\sim$5$\times10^4$ PS1 sources with HST COSMOS morphological classifications and assess its performance using $\sim$4$\times10^6$ sources with Sloan Digital Sky Survey (SDSS) spectra and $\sim$2$\times10^8$ \textit{Gaia} sources. We construct 11 "white flux" features, which combine PS1 flux and shape measurements across 5 filters, to increase the signal-to-noise ratio relative to any individual filter. The RF model is compared to 3 alternative models, including the SDSS and PS1 photometric classification models, and we find that the RF model performs best. By number the PS1 catalog is dominated by faint sources ($m\gtrsim21\,$mag), and in this regime the RF model significantly outperforms the SDSS and PS1 models. For time-domain surveys, identifying unresolved sources is crucial for inferring the Galactic or extragalactic origin of new transients. We have classified $\sim$1.5$\times10^9$ sources using the RF model, and these results are used within the Zwicky Transient Facility real-time pipeline to automatically reject stellar sources from the extragalactic alert stream.
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