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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 We study a magnitude-limited sample of 36 broad-lined type Ic supernovae (SNe Ic-BL) from the Zwicky Transient Facility Bright Transient Survey (detected between 2018 March and 2021 August), which is the largest systematic study of SNe Ic-BL done in literature thus far. We present the light curves (LCs) for each of the SNe and analyze the shape of the LCs to derive empirical parameters, along with the explosion epochs for every event. The sample has an average absolute peak magnitude in therband of ${\overline{\mathit{M}}}_{\mathrm{r},\max }=-18.51\pm 0.15$mag. Using spectra obtained around peak light, we compute expansion velocities from the Feii5169 Å line for each event with high enough signal-to-noise ratio spectra, and find an average value of $\overline{v_{\mathrm{ph}}}=16,100\pm 1100$km s⁻¹. We also compute bolometric LCs, study the blackbody temperature and radii evolution over time, and derive the explosion properties of the SNe. The explosion properties of the sample have average values of ${\overline{\mathit{M}}}_{\mathrm{Ni}}={0.37}_{-0.06}^{+0.08}{\mathit{M}}_{\odot }$, ${\overline{\mathit{M}}}_{\mathrm{ej}}={2.45}_{-0.41}^{+0.47}{\mathit{M}}_{\odot }$, and ${\overline{\mathit{E}}}_{\mathrm{K}}=\left({4.02}_{-1.00}^{+1.37}\right)\times {10}^{51}$erg. Thirteen events have radio observations from the Very Large Array, with eight detections and five non-detections. We find that the populations that have radio detections and radio non-detections are indistinct from one another with respect to their optically inferred explosion properties, and there are no statistically significant correlations present between the events’ radio luminosities and optically inferred explosion properties. This provides evidence that the explosion properties derived from optical data alone cannot give inferences about the radio properties of SNe Ic-BL and likely their relativistic jet formation mechanisms.