Classifying the Sky with ZTF

Oregon Convention Center 

The Zwicky Transient Facility (ZTF) has successfully collected hundreds of data points each for over a billion sources in the Northern sky over the last few years, creating a rich dataset for astronomical analysis. Leveraging advanced machine learning techniques, specifically deep neural networks (DNN) and XGBoost, we've developed binary classifiers to sift through this vast time-series data, enhancing our ability to classify astronomical phenomena accurately. We outline the methodologies employed, challenges encountered, and the innovative solutions devised. Additionally, we explore the integration of data from various surveys, the impact of observational cadences, and the implications for future surveys like LSST/Rubin, particularly in the context of transfer learning and the pursuit of fainter celestial objects and outliers.