tdescore: An Accurate Photometric Classifier for Tidal Disruption Events

Abstract

Abstract Optical surveys have become increasingly adept at identifying candidate tidal disruption events (TDEs) in large numbers, but classifying these generally requires extensive spectroscopic resources. Here we present tdescore, a simple binary photometric classifier that is trained using a systematic census of ∼3000 nuclear transients from the Zwicky Transient Facility (ZTF). The sample is highly imbalanced, with TDEs representing ∼2% of the total. tdescore is nonetheless able to reject non-TDEs with 99.6% accuracy, yielding a sample of probable TDEs with recall of 77.5% for a precision of 80.2%. tdescore is thus substantially better than any available TDE photometric classifier scheme in the literature, with performance not far from spectroscopy as a method for classifying ZTF nuclear transients, despite relying solely on ZTF data and multiwavelength catalog cross matching. In a novel extension, we use “Shapley additive explanations” to provide a human-readable justification for each individual tdescore classification, enabling users to understand and form opinions about the underlying classifier reasoning. tdescore can serve as a model for photometric identification of TDEs with time-domain surveys, such as the upcoming Rubin observatory.