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.

DBSP_DRP reduces data from the Palomar spectrograph DBSP. Built on top of PypeIt (ascl:1911.004), it automates the reduction, fluxing, telluric correction, and combining of the red and blue sides of one night's data. The pipeline also provides several GUIs for easier control of the reduction, with one for selecting which data to reduce, and verifying the correctness of FITS headers in an editable table. Another GUI manually places traces for a sort of manually "forced" spectroscopy with the -m option, and after manually placing traces, manually selects sky regions and tweaks the FWHM of the manual traces.