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The classification of variable objects provides insight into a wide varietyof astrophysics ranging from stellar interiors to galactic nuclei. The ZwickyTransient Facility (ZTF) provides time series observations that record thevariability of more than a billion sources. The scale of these datanecessitates automated approaches to make a thorough analysis. Building onprevious work, this paper reports the results of the ZTF Source ClassificationProject (SCoPe), which trains neural network and XGBoost machine learning (ML)algorithms to perform dichotomous classification of variable ZTF sources usinga manually constructed training set containing 170,632 light curves. We findthat several classifiers achieve high precision and recall scores, suggestingthe reliability of their predictions for 112,476,749 light curves across 40 ZTFfields. We also identify the most important features for XGB classification andcompare the performance of the two ML algorithms, finding a pattern of higherprecision among XGB classifiers. The resulting classification catalog isavailable to the public, and the software developed for SCoPe is open-sourceand adaptable to future time-domain surveys.

The ZTF Source Classification Project: III. A Catalog of Variable Sources