braai (Bogus/Real Adversarial AI) performs deep-learning real/bogus classification for the Zwicky Transient Facility (ZTF), separating genuine astrophysical events and objects from false positive detections. It uses a convolutional neural network to enable efficient automated detection of flux transients, recurring flux-variable sources, and moving objects in large-scale astronomical survey data. In production, it achieves low false negative and false positive rates.

Tails identifies and localizes comets in image data from the Zwicky Transient Facility (ZTF), a robotic optical sky survey, using deep-learning with a custom EfficientDet-based architecture. It detects comets in single images in near real time, rather than requiring multiple epochs as in traditional methods. In production, Tails achieves 99% recall, a false positive rate below 0.01%, and 1–2 pixel root mean square error in the predicted position.

penquins enables programmatic querying of the Kowalski multi-survey data archive and alert broker, which primarily serves alerts from the Zwicky Transient Facility. It provides a programmatic interface to submit queries, retrieve alert data and associated metadata from Kowalski’s backend, and integrate those queries into automated analysis workflows. The library is intended to streamline access to brokered time‑domain data for downstream scientific analysis.