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.

SNITCH (bayeSian iNference given emIssion and absorpTion features of quenChing Histories) performs Bayesian inference of star formation histories using measured emission and absorption spectral features. The code ingests equivalent widths and spectral indices (e.g., EW[Hα], Dₙ4000, Hβ, Hδₐ, Mg Fe′) and, via a pre-computed look-up table generated with FSPS models, returns posterior estimates of parameters such as metallicity, quenching time and quenching rate. It uses emcee (ascl:1303.002) to explore parameter space, produces diagnostic plots of MCMC walkers and corner plots, and is configurable to suit custom spectral parameter sets and lookup-table ranges. Designed for application to integrated‐light or IFU galaxy spectra, SNITCH enables flexible adaptation for a variety of star‐formation-history studies.

StarPy derives the quenching star formation history (SFH) of a single galaxy through the Bayesian Markov Chain Monte Carlo method code emcee (ascl:1303.002). The sample function implements the emcee EnsembleSampler function for the galaxy colors input. Burn-in is run and calculated for the length specified before the sampler is reset and then run for the length of steps specified. StarPy provides the ability to use the look-up tables provided or creating your own.