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.

SATCHEL (Search Algorithm for Transits in the Citizen science Hunt for Exoplanets in Lightcurves) searches for individual signals of interest in time-series data classified through crowdsourcing. The pipeline was built for the purpose of finding long-period exoplanet transit signals in Kepler photometric time-series data, but may be adapted for searches for any kind of one-dimensional signals in crowdsourced classifications.