Machine Learning Classification of Time-varying Astrophysical Sources from the Zwicky Transient Facility

The Zwicky Transient Facility (ZTF) surveys the full Northern sky every two days, providing plentiful observations of time-varying astrophysical sources. The data from this photometric survey can reveal the intrinsic nature of sources, but the sheer amount of data prohibits a fully manual classification process. In this talk, I describe the background and current progress of the ZTF Source Classification Project (SCoPe), which employs a combination of human input and machine learning (ML) algorithms to classify sources. The project trains ML algorithms using an active learning approach, in which subsets of automated classifications are evaluated and revised by human experts before being added to a new iteration of training. By progressively improving our algorithms in this way, we advance toward our goal of providing a reliable public catalog of source classifications for the full ZTF dataset, enabling further research along multiple avenues.