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.

Zoobot classifies galaxy morphology with Bayesian CNN. Deep learning models were trained on volunteer classifications; these models were able to both learn from uncertain volunteer responses and predict full posteriors (rather than point estimates) for what volunteers would have said. The code reproduces and improves Galaxy Zoo DECaLS automated classifications, and can be finetuned for new tasks.