pht-ml trains deep learning models to classify planetary transits from TESS light curves using preprocessed PDCSAP fluxes from .fits files. The pipeline applies binning, normalization, and optional data augmentations, and it can incorporate external synthetic light curves alongside label files. Code in the data module handles loading of light curves and label tables when provided. Experiments and hyperparameters are configured via command-line arguments and run through a main training script.

LATTE identifies, vets and characterizes signals in TESS lightcurves to weed out instrumental and astrophysical false positives. The program performs a fast in-depth analysis of targets that have already been identified as promising candidates by the main TESS pipelines or via alternative methods such as citizen science. The code automatically downloads the data products for any chosen TIC ID (short or long cadence TESS data) and produces a number of diagnostic plots that are compiled in a concise report.