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In the era of large photometric surveys, the importance of automated andaccurate classification is rapidly increasing. Specifically, the separation ofresolved and unresolved sources in astronomical imaging is a critical initialstep for a wide array of studies, ranging from Galactic science to large scalestructure and cosmology. Here, we present our method to construct a large, deepcatalog of point sources utilizing Pan-STARRS1 (PS1) 3$\pi$ survey data, whichconsists of $\sim$3$\times10^9$ sources with $m\lesssim23.5\,$mag. We develop asupervised 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 classificationsand assess its performance using $\sim$4$\times10^6$ sources with Sloan DigitalSky Survey (SDSS) spectra and $\sim$2$\times10^8$ \textit{Gaia} sources. Weconstruct 11 "white flux" features, which combine PS1 flux and shapemeasurements across 5 filters, to increase the signal-to-noise ratio relativeto any individual filter. The RF model is compared to 3 alternative models,including the SDSS and PS1 photometric classification models, and we find thatthe RF model performs best. By number the PS1 catalog is dominated by faintsources ($m\gtrsim21\,$mag), and in this regime the RF model significantlyoutperforms the SDSS and PS1 models. For time-domain surveys, identifyingunresolved sources is crucial for inferring the Galactic or extragalacticorigin of new transients. We have classified $\sim$1.5$\times10^9$ sourcesusing the RF model, and these results are used within the Zwicky TransientFacility real-time pipeline to automatically reject stellar sources from theextragalactic alert stream.

A Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources: Application in the ZTF Real-Time Pipeline