Temporal Stamp Classifier: Classifying Short Sequences of Astronomical Alerts
This work addresses classification of transient astronomical events for astronomers, but it is incremental as it builds on existing stamp classifier models with architectural tweaks.
The authors tackled the problem of classifying astronomical objects from short sequences of ZTF alerts, achieving approximately 98% accuracy in distinguishing AGN, SNe, and VS with 2 to 5 detections.
In this work, we propose a deep learning-based classification model of astronomical objects using alerts reported by the Zwicky Transient Facility (ZTF) survey. The model takes as inputs sequences of stamp images and metadata contained in each alert, as well as features from the All-WISE catalog. The proposed model, called temporal stamp classifier, is able to discriminate between three classes of astronomical objects: Active Galactic Nuclei (AGN), Super-Novae (SNe) and Variable Stars (VS), with an accuracy of approximately 98% in the test set, when using 2 to 5 detections. The results show that the model performance improves with the addition of more detections. Simple recurrence models obtain competitive results with those of more complex models such as LSTM.We also propose changes to the original stamp classifier model, which only uses the first detection. The performance of the latter model improves with changes in the architecture and the addition of random rotations, achieving a 1.46% increase in test accuracy.