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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 Type Ia supernovae (SNe Ia) are thermonuclear explosions of degenerate white dwarf stars destabilized by mass accretion from a companion star 1 , but the nature of their progenitors remains poorly understood. A way to discriminate between progenitor systems is through radio observations; a non-degenerate companion star is expected to lose material through winds 2 or binary interaction 3 before explosion, and the supernova ejecta crashing into this nearby circumstellar material should result in radio synchrotron emission. However, despite extensive efforts, no type Ia supernova (SN Ia) has ever been detected at radio wavelengths, which suggests a clean environment and a companion star that is itself a degenerate white dwarf star 4,5 . Here we report on the study of SN 2020eyj, a SN Ia showing helium-rich circumstellar material, as demonstrated by its spectral features, infrared emission and, for the first time in a SN Ia to our knowledge, a radio counterpart. On the basis of our modelling, we conclude that the circumstellar material probably originates from a single-degenerate binary system in which a white dwarf accretes material from a helium donor star, an often proposed formation channel for SNe Ia (refs.  6,7 ). We describe how comprehensive radio follow-up of SN 2020eyj-like SNe Ia can improve the constraints on their progenitor systems.