escatter.py performs Monte Carlo simulations of electron scattering events. The code was developed to better understand the emission lines from the interacting supernova SN 2021adxl, specifically the blue excess seen in the Hα 6563A emission line. escatter follows a photon that was formed in a thin interface between the supernova ejecta and surrounding material as it travels radially outwards through the dense material, scattering electrons outwards until it reaches an optically thin region, and plots a histogram of the emergent photons.

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.

The Bayesian-based Gaussian Process model AsteroGaP (Asteroid Gaussian Processes) fits sparsely-sampled asteroid light curves. By utilizing a more flexible Gaussian Process framework for modeling asteroid light curves, it is able to represent light curves in a periodic but non-sinusoidal manner.

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.