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Abstract The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series observations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. Building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGBoost (XGB) machine-learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. The resulting classification catalog is available to the public, and the software developed forSCoPeis open source and adaptable to future time-domain surveys. 

ABSTRACT The ever-increasing sensitivity of the network of gravitational-wave detectors has resulted in the accelerated rate of detections from compact binary coalescence systems in the third observing run of Advanced LIGO and Advanced Virgo. Not only has the event rate increased, but also the distances to which phenomena can be detected, leading to a rise in the required sky volume coverage to search for counterparts. Additionally, the improvement of the detectors has resulted in the discovery of more compact binary mergers involving neutron stars, revitalizing dedicated follow-up campaigns. While significant effort has been made by the community to optimize single telescope observations, using both synoptic and galaxy-targeting methods, less effort has been paid to coordinated observations in a network. This is becoming crucial, as the advent of gravitational-wave astronomy has garnered interest around the globe, resulting in abundant networks of telescopes available to search for counterparts. In this paper, we extend some of the techniques developed for single telescopes to a telescope network. We describe simple modifications to these algorithms and demonstrate them on existing network examples. These algorithms are implemented in the open-source software gwemopt, used by some follow-up teams, for ease of use by the broader community.